# LLMS > Simplify, safeguard, and accelerate AI value with open source. --- ## Pages - [Easy Deployment](https://stage.anaconda.com/capabilities/easy-deployment): Reduce your organization's dependency on MLOps and empower your data scientists to innovate at unprecedented speeds. - [Tech Talk: CAIO Unplugged](https://10.2.107.56:8443/thank-you/tech-talk-caio-unplugged): Join us for an exclusive Tech Talk with Anaconda’s Chief AI and Innovation Officer and Co-founder, Peter Wang. In this... - [Automate Your Analysis with Snowflake and Anaconda](https://10.2.107.56:8443/thank-you/automate-analysis-snowflake-anaconda-on-demand-video): Learn the power of Snowflake SQL and Anaconda Python packages to simplify tasks that often require advanced tools or technical... - [The Women Illuminating the Path to AI Success](https://10.2.107.56:8443/thank-you/women-illuminating-path-ai-success): Join Forrester analyst Brandon Purcell and women tech leaders as they share proven strategies for building organization-wide AI capabilities. This... - [Events](https://10.2.107.56:8443/events): Events - [Terms and Policies](https://10.2.107.56:8443/legal/terms): All Anaconda legal terms for your use of our different websites, Offerings, and separate products or services provided by Anaconda. - [Anaconda Legal](https://10.2.107.56:8443/legal): Access our standard agreements, policies, and terms governing the use of our Platform and Offerings, as well as our Privacy Center and Trust Center. - [Jobs](https://10.2.107.56:8443/about-us/careers/jobs): See open roles across departments and countries.Read job descriptions and learn how to apply to available jobs at Anaconda, the operating system for AI. - [Leadership](https://10.2.107.56:8443/about-us/leadership): Anaconda's executive team includes best in class product and operational leaders, and is focused on scaling Anaconda to new heights. - [Secure Governance](https://10.2.107.56:8443/ai-platform/secure-governance): Build securely with enterprise governance, deploy faster with vetted models, and reduce risk with comprehensive security - [Actionable Insights](https://stage.anaconda.com/ai-platform/actionable-insights): Download and experiment locally with a curated repository of open-source large-language models with Anaconda AI Navigator. - [Trusted Distribution](https://stage.anaconda.com/ai-platform/trusted-distribution): Download and experiment locally with a curated repository of open-source large-language models with Anaconda AI Navigator. - [AI Platform](https://stage.anaconda.com/ai-platform): Download and experiment locally with a curated repository of open-source large-language models with Anaconda AI Navigator. - [Newsletter Sign Up](https://stage.anaconda.com/newsletter): Be the First To Know Sign up for the Anaconda newsletter to be the first to hear about exciting news... - [Technology Partners](https://10.2.107.56:8443/partners/technology): Technology Partners Our technology partners empower AI advancement through delivering the open-source Python and R tooling that simplifies and secures... - [Careers](https://stage.anaconda.com/about-us/ai-python-careers-anaconda): Join Anaconda's team shaping the future of AI with open source. Enjoy exceptional benefits, career growth, and a remote-first culture in a diverse, award-winning company. Apply today! - [Free Download](https://stage.anaconda.com/download-2): Download Anaconda's open-source Distribution today. Discover the easiest way to perform Python/R data science and machine learning on a single machine. - [Contact Us](https://stage.anaconda.com/contact): Find the plan that's right for you by contacting the Anaconda sales team. Learn how to contact Support, and get contact info for Anaconda. - [Homepage](https://stage.anaconda.com/): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Talk to an Expert](https://10.2.107.56:8443/talk-to-an-expert): Learn how Anaconda enables enterprise open-source AI innovation with a platform designed to support compliance, security, and governance requirements. - [Products](https://10.2.107.56:8443/products): The hub for data science and AI collaboration. Source, build, and deploy with ease, using leading-edge tools that take your work from idea to integration. - [Professional Services](https://stage.anaconda.com/professional-services): Solve data science and machine learning challenges alongside Anaconda's consultants, developers, and engineers. See what the professional services team can do for you. - [Our Open-Source Commitment](https://10.2.107.56:8443/our-open-source-commitment): Anaconda has supported open-source innovation and project maintenance in the form of employee time, direct donations, event sponsorships, and more. Learn More - [Open Source](https://10.2.107.56:8443/open-source): We are proud to distribute and contribute to a variety of open-source projects. Technologies for Data Science - [IT Admins](https://10.2.107.56:8443/it-admins): IT Admins, learn how to stop vulnerabilities, not workflows. - [Infographics](https://stage.anaconda.com/resources/infographics): Infographics - [Package Security Manager](https://stage.anaconda.com/products/package-security-manager): Anaconda's Package Security Manager presents a holistic and forward-thinking approach to managing package security within data science and machine learning environments. Learn More - [On-Premises LLM](https://10.2.107.56:8443/products/on-premises-llm): Anaconda provides on-premises deployment options for LLMs, enabling organizations to securely host and utilize these powerful AI models within their own infrastructure - [Notebooks](https://stage.anaconda.com/products/notebooks): Notebooks allow anyone, anywhere to spin up powerful data science projects directly from your browser, with all the packages and computing power you need. - [Anaconda Navigator](https://10.2.107.56:8443/products/navigator): Work with the packages you want, install in any environment, and run and update them without needing to type conda commands in a terminal window. Learn More - [Models](https://10.2.107.56:8443/products/models): Decrease risk and increase productivity with Anaconda’s repository of curated, rigorously tested, and verified data and models. - [Solutions for Practitioners](https://10.2.107.56:8443/practitioners): Moving from idea to solution involves a complex network of dependencies and environments and can delay business insights. Anaconda simplifies that complexity. - [Installation Success](https://10.2.107.56:8443/installation-success): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Security and Compliance](https://10.2.107.56:8443/security-compliance): Anaconda is ISO 27001 certified and prioritizes security through data encryption, access control, and an organizational commitment to secure practices. - [TOS Access](https://10.2.107.56:8443/tos-access): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [TOS Error](https://10.2.107.56:8443/tos-error): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [ToS Access Notice](https://10.2.107.56:8443/tos-mirroring): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Download Success - post download](https://stage.anaconda.com/download-success): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Go-To-Market Resources: Partners](https://10.2.107.56:8443/partner-brand-kit): The go-to-market kit includes assets, collateral, and guidelines for adding Anaconda to your partner directory, enable your sales teams, and engage your audience. - [Anaconda Toolbox for Microsoft Excel](https://10.2.107.56:8443/products/anaconda-toolbox): Anaconda Toolbox for Excel enhances Microsoft Excel with a suite of tools built to make coding in Python even easier. - [Pricing for Individuals and Organizations](https://stage.anaconda.com/pricing): Flexible pricing plans that scale with your AI needs. Free, Starter, Business, and Enterprise tiers offer tailored solutions for your data science and ML projects. - [Download Success](https://stage.anaconda.com/download-2/download-success): Download Now Download Anaconda Distribution or Miniconda by choosing the proper installer for your machine. Learn the difference from our... - [Anaconda AI Navigator](https://stage.anaconda.com/products/ai-navigator): Download and execute curated open-source large language models(LLMs) secure on your desktop with Anaconda AI Navigator. - [Business Plan](https://10.2.107.56:8443/pricing/business): Anaconda Business Plan allows your data science, ML, and AI teams to us - [Topics](https://10.2.107.56:8443/topics): Topics - [Enterprise Plan](https://10.2.107.56:8443/enterprise): Unlock data's business value faster, with thousands of multi-platform, multi-language open-source packages curated and secured by Python experts, with the Enterprise plan. - [Podcast](https://10.2.107.56:8443/resources/podcast): Podcasts - [Case Study](https://10.2.107.56:8443/resources/case-study): Case Studies - [Guides](https://10.2.107.56:8443/guides): Guides - [Report](https://10.2.107.56:8443/resources/report): Reports - [Video](https://10.2.107.56:8443/resources/video): Videos - [Whitepaper](https://10.2.107.56:8443/resources/whitepaper): White Paper - [Solutions](https://10.2.107.56:8443/solutions) - [Team Collaboration](https://stage.anaconda.com/capabilities/team-collaboration): Avoid Duplication of Efforts and Misalignment of Priorities. Effective team collaboration is essential for leveraging expertise, sharing knowledge, and maintaining organized data science workflows. Learn More - [Machine Learning](https://stage.anaconda.com/capabilities/machine-learning): Anaconda's machine learning capabilities encompass a diverse array of algorithms and tools, empowering users to build, train, and deploy sophisticated models. - [On-Demand Infrastructure](https://stage.anaconda.com/capabilities/capability-on-demand-infrastructure): On demand infrastructure enables organizations to effectively leverage compute resources and accelerate innovation. - [Visualization](https://10.2.107.56:8443/capability/visualization): Anaconda can bridge the gap between your data science and IT teams. Get MLops workflows that drive value from your AI initiatives - [Version Control](https://stage.anaconda.com/capabilities/capability-version-control): Anaconda ensures consistency in collaborative environments, managing complex project dependencies and reproducing analysis, and experimental results. Learn More - [Reproducibility](https://stage.anaconda.com/capabilities/reproducibility): Anaconda fosters AI reproducibility by offering version control and dependency management, ensuring consistent environments for model development and deployment - [Secure Package Management](https://stage.anaconda.com/capabilities/secure-package-management): Secure Open Source Software. Implement robust security tools and practices to protect your organization keeping you on the forefront of technological solutions. - [Channel and Service Partners](https://10.2.107.56:8443/partners/channel-and-services): Channel and Service Partners Join our network of resellers, distributors, referral partners, and services partners — and foster AI innovation... - [Learning](https://10.2.107.56:8443/learning): Learn Python, AI, and data science your way. Anaconda Learning offers self-paced courses, professional certifications, and hands-on coding practice. - [Python In Excel](https://stage.anaconda.com/partners/python-in-excel): Python in Excel Harness the Power of Python in Microsoft Excel’s Familiar Interface Experience Python In Excel Next level data... - [Become a Partner](https://stage.anaconda.com/partners/become-a-partner): Become a Partner Tap into our vast network and grow with us Become a Partner Anaconda Partnership Technology Build AI... - [Partners](https://10.2.107.56:8443/partners): Anaconda Partner Network Empowering AI Advancement Together Become a Partner 0 M+ Users our partners can reach 0 M Organizations:... - [Industries](https://stage.anaconda.com/industries-2): Explore how Anaconda is transforming industries worldwide, including the fields of healthcare, financial services, government, manufacturing, and technology. - [Accessibility](https://10.2.107.56:8443/accessibility): Learn how Anaconda ensures digital accessibility for all users. Discover our WCAG 2.1 Level AA compliance and commitment to inclusive design. - [Technology](https://10.2.107.56:8443/industries/technology): Anaconda enables AI by providing solutions to build applications for risk management, algorithmic trading, and customer insights. Learn more - [Trusted Tools to Revolutionize AI in Manufacturing](https://stage.anaconda.com/industries-2/manufacturing): Explore how Anaconda’s solutions can help enhance efficiency, quality control, and innovation for smarter, more sustainable manufacturing. Learn more - [Anaconda for Education: Accelerate Your AI Learning](https://stage.anaconda.com/industries-2/education): Anaconda for Education offers users with academic emails free access to premium features, including tools, resources, and cloud storage to enhance learning. - [Healthcare](https://10.2.107.56:8443/industries/healthcare): Discover developing AI in healthcare with Anaconda to improve patient care, and operational efficiency, and ensure data privacy in the medical field. Learn more - [Financial Services](https://10.2.107.56:8443/industries/financial-services): Anaconda enables AI for financial services and banks by providing solutions to build applications for risk management, algorithmic trading, and customer insights. Learn more - [Government](https://10.2.107.56:8443/industries/government): Explore AI solutions for government agencies to enhance public services, ensure data security, and drive operational efficiency with Anaconda. - [Deploy Inference APIs](https://stage.anaconda.com/capabilities/capability-deply-inference-apis): APIs integrate AI models into applications, web services, and IT infrastructure and let developers rapidly develop and deploy, protect data, and collaborate. - [Data Management Solutions](https://stage.anaconda.com/capabilities/capability-data-management): Anaconda offers the all-in-one data management solution, integrating acquisition, analysis, and collaboration through a comprehensive suite of tools and libraries. - [Dashboards](https://stage.anaconda.com/capabilities/dashboards): Anaconda empowers data scientists and analysts to effortlessly deploy captivating, interactive dashboards using Panel. Learn More - [Anaconda Assistant](https://10.2.107.56:8443/capability/anaconda-assistant): With Anaconda Assistant, an AI-powered chatbot, get help writing, analyzing, and debugging code directly in Notebooks. Learn More - [Air-Gapped Environment Capabilities](https://10.2.107.56:8443/capability/air-gap): Work effectively and safely in a secure environment with Anaconda air gap capabilities. Install offline, manage packages locally, create custom environments, and securely transfer data. Learn More. - [AI Governance](https://10.2.107.56:8443/capability/governance): Anaconda’s AI & data science governance allows users to establish clear policies and controls in their data science work. Learn More - [Application Deployment](https://stage.anaconda.com/capabilities/application-deployment): Anaconda streamlines app deployment by providing tools for packaging and distributing data science applications, ensuring seamless deployment across different environments and platforms - [Generative AI](https://stage.anaconda.com/capabilities/capability-gen-ai): Anaconda's GenAI capabilities leverage advanced ML algorithms to automate and optimize data science workflows, enhancing productivity and accelerating the generation of business insights. Learn More - [Error Tracking & Logging](https://stage.anaconda.com/capabilities/error-tracking-logging): Anaconda's error tracking capabilities allow users to effectively identify, diagnose, and resolve errors in data science projects. - [Webinar](https://10.2.107.56:8443/resources/webinar): Webinars - [Resources](https://10.2.107.56:8443/resources): Resources to Help You Code to the Next Level Latest Resources Anaconda Blog - [Capability](https://stage.anaconda.com/capabilities): Jumpstart your AI projects, create reproducible results, or fine-tune your security governance program. Anaconda offers a wide range of capabilities to meet your needs. Learn More - [In the News](https://10.2.107.56:8443/press/in-the-news): In the News - [Newsroom](https://10.2.107.56:8443/newsroom): Newsroom In the News See All In the News Press Releases See All Press Releases - [Press](https://10.2.107.56:8443/press): Press Release - [About Us](https://10.2.107.56:8443/about-us): Sitting at the center of the AI revolution, Anaconda empowers our customers and community with open source. - [Technical Notes](https://10.2.107.56:8443/blogs/technical-notes): Technical Notes View All Product Perspectives News Technical Notes - [News](https://10.2.107.56:8443/blogs/news): News - [Perspectives](https://10.2.107.56:8443/blogs/perspectives): Perspectives - [Product](https://10.2.107.56:8443/blogs/product): Product - [Blog](https://10.2.107.56:8443/blog): Anaconda Blog Featured View All Product Perspectives News Technical Notes Product See All Product Perspectives See All Perspectives News See... --- ## Posts - [The AI Governance Paradox](https://stage.anaconda.com/blog/the-ai-governance-paradox): As every March Madness fan knows, athletic talent and coaching are key, but it’s how they come together as a unit that determines a team’s success. Known… - [Committing to the Advancement of AI with Open Source](https://stage.anaconda.com/open-source-ai-commitment-series-c-funding): Anaconda co-founder Peter Wang on our $150M Series C funding and deepening investment in open source AI. See how we're building the future of Python and AI. - [Building AI-Powered Financial Services: Your Strategic Guide to Modern Tools and Platforms](https://stage.anaconda.com/building-ai-powered-financial-services-strategic-guide): Transform your financial institution with AI using unified platforms and proven frameworks. See how banks achieve 119% ROI. Get started today! - [Intel Mac Package Support: End of an Era](https://stage.anaconda.com/intel-mac-package-support-end-of-an-era): Anaconda announcement of the deprecation of Intel Mac (osx-64) package builds, effective August 15, 2025. - [Build a Scikit-Learn Model Using Snowpark for Python](https://10.2.107.56:8443/blog/scikit-learn-model-using-snowpark): Learn to build scikit-learn models with Snowpark for Python. Clean data, engineer features, and deploy predictions directly in Snowflake using UDFs. - [Using Anaconda without Worry: Enabling ToS compliance with the conda-anaconda-tos plugin](https://stage.anaconda.com/using-anaconda-without-worry-enabling-tos-compliance-with-the-conda-anaconda-tos-plugin): Learn about the conda-anaconda-tos plugin that keeps you informed about Anaconda's Terms of Service changes and ensures seamless ToS acceptance in your conda workflow. Open source and CI-friendly. - [Anaconda Recognized for Excellence in AI Innovation​](https://10.2.107.56:8443/blog/anaconda-recognized-for-excellence-in-ai-innovation): We’re thrilled to share that Anaconda has been named a winner in the 2025 Artificial Intelligence Excellence Awards! The Business... - [Tiny Giants in AI: Benchmarking Specialized SQL Models Against Industry Heavyweights](https://10.2.107.56:8443/blog/evaluating-small-ai-models): In an era where massive language models dominate headlines, a fascinating trend is emerging: highly specialized smaller models are proving... - [The Shadow AI Crisis: Why Enterprise Governance Can’t Wait Any Longer](https://10.2.107.56:8443/blog/shadow-ai-crisis-in-the-enterprise): Survival instinct is driving AI application development at unprecedented rates. As major firms like PwC cut 1,500 jobs while pouring... - [Introducing the Anaconda Community Channel: Expanding Your Open Source Arsenal While Maintaining Enterprise Control](https://10.2.107.56:8443/blog/introducing-the-anaconda-community-channel-expanding-your-open-source-arsenal-while-maintaining-enterprise-control): The Community Channel—a new addition to the Anaconda AI Platform that expands your organization’s access to more open source packages while preserving the governance and security controls your organization depends on. - [New Release: Anaconda Distribution 2025.06](https://stage.anaconda.com/new-release-anaconda-distribution-2025-06): We are excited to announce the 2025. 06 release of the Anaconda Distribution installer, which includes: Python – the most... - [Recognizing International Women’s Day and Diversity in Tech](https://10.2.107.56:8443/blog/international-womens-day-2021): In honor of International Women’s Day, I want to tell you about my experience as a woman in tech and... - [How Anaconda and Databricks Are Solving Enterprise AI’s Biggest Open-Source Challenge](https://stage.anaconda.com/blog/how-anaconda-and-databricks-are-solving-enterprise-ai-biggest-open-source-challenge): Anaconda Toolbox is a Microsoft Excel add-in that brings AI-powered Anaconda Assistant, curated data catalogs, and cloud features to Python in Excel users. - [Introducing anaconda-auth – An Easier Way To Access Anaconda’s Premium Repo And Your Organization’s Internal Channels](https://10.2.107.56:8443/blog/introducing-anaconda-auth): Streamline access to Anaconda Premium repositories with our new CLI-based token management workflow. Install, configure, and authenticate with a single command - anaconda token install. - [A Letter to Our Community from Peter Wang, Co-Founder of Anaconda](https://10.2.107.56:8443/blog/letter-to-our-community): Dear Anaconda Community, I want to address you directly regarding the changes and growth Anaconda has experienced and the updated... - [Anaconda Assistant for conda now in Private Beta](https://10.2.107.56:8443/blog/anaconda-assistant-for-conda-private-beta): You may already be familiar with the Anaconda Assistant, the AI coding assistant from Anaconda Toolbox, available on https://nb. anaconda.... - [Gratitude and Growth: Reflecting on 2023 and Embracing the Promise of 2024](https://10.2.107.56:8443/blog/gratitude-and-growth-reflecting-on-2023-and-embracing-the-promise-of-2024): As the holiday season draws near, we at Anaconda take a moment to reflect on the past year with profound... - [Sustaining and Advancing Jupyter: NbClassic 1.3 and Jupyter-Fsspec 0.4](https://10.2.107.56:8443/blog/sustaining-and-advancing-jupyter-nbclassic-1-3-and-jupyter-fsspec-0-4): In today’s AI landscape, accessible and powerful tools can define anyone’s ability to learn, innovate, and deliver value whether you’re... - [Quick Start Environments: Simplifying AI Development for Practitioners](https://10.2.107.56:8443/blog/quick-start-enviornments-simplifying-ai-development-for-practitioners): As data scientists, ML engineers, and developers, we’ve all faced the frustration of environment setup. The hours spent configuring dependencies,... - [Unifying the Open Source AI Journey: Introducing the Anaconda AI Platform](https://10.2.107.56:8443/blog/introducing-the-anaconda-ai-platform): The Anaconda AI Platform unifies capabilities our users already know and love to help streamline their organization’s AI and data science initiatives with open source. Learn More - [You Spoke, We Listened: Anaconda is Free for Academic, Research & Non-Profit Users](https://10.2.107.56:8443/blog/anaconda-is-free-for-academic-research-and-non-profit-users): Anaconda is Free for Academic, Research & Non-Profit Users. Learn more. - [Enterprise Open Source AI: Navigating Risk, Securing Innovation, and Owning Your Destiny](https://10.2.107.56:8443/blog/navigate-open-source-ai): AI adoption has reached critical mass: 99% of organizations are either using AI or actively exploring it, with nearly half... - [Navigating the Evolving AI Landscape: How Anaconda is Reshaping Data Science and Machine Learning Platforms](https://10.2.107.56:8443/blog/anaconda-gartner-voice-of-the-customer-report): The data science and machine learning (DSML) landscape continues to evolve at a rapid pace, with organizations facing increasingly complex... - [Illuminating Our Energy Future: How AI and Data Visualization Can Drive Sustainability Insights](https://stage.anaconda.com/blog/illuminate-data-lumen-ai): At Anaconda, we believe that accessible tools for data analysis empower individuals to discover insights that can lead to meaningful... - [Q&A With Anaconda Experts: How Do You Become a Data Scientist?](https://10.2.107.56:8443/blog/how-do-you-become-a-data-scientist): There is not a single linear path for a career in data science. As a named discipline, data science didn’t... - [Ten Techniques for Machine Learning Visualization](https://stage.anaconda.com/top-ten-techniques-for-machine-learning-visualization): As part of any data science project, data visualization plays an important part in order to learn more about the... - [How to Build AI Chatbots with Mistral and Llama2](https://10.2.107.56:8443/blog/how-to-build-ai-chatbots-with-mistral-and-llama2): This complete LLM project focuses on combining Mistral 7B and Llama2 for a high-performing AI chatbot on a local device. - [How to Run Python in Excel on a Mac](https://10.2.107.56:8443/blog/how-to-run-python-in-excel-on-a-mac): Learn how to set up a Windows virtual machine on a Mac so you can run Python in Excel to your heart’s content! - [ChatGPT: Is Adding AI Writers To Your Content Production Process Worth It?](https://10.2.107.56:8443/blog/chatgpt-is-adding-ai-writers-to-your-content-production-process-worth-it): This is the first article in a series of two. The second article is ChatGPT: AI-Assisted Writing Pros, Cons, and Tips to 3x Content Production . We have… - [Open Source Growth at Anaconda in 2025: Building the Foundations for AI Innovation](https://10.2.107.56:8443/blog/anaconda-oss-growth-update-2025): Open source software powers the data science and AI revolution, providing the essential tools that researchers, developers, and organizations rely... - [R Language Support Now in Anaconda Code for Excel](https://10.2.107.56:8443/blog/anaconda-code-brings-r-to-excel): We’re thrilled to announce the latest addition to Anaconda Toolbox: R language support in Anaconda Code! With this beta release,... - [Anaconda Learning Domain Change Frequently Asked Questions](https://10.2.107.56:8443/blog/anaconda-domain-change-frequently-asked-questions): Anaconda Learning is moving to new Anaconda.com/learning domain, consolidating our platform into a single location and migrating to a new learning management system. Read the latest FAQs. - [Anaconda Toolbox Brings AI Assistant, No-Code Development to Python in Excel](https://stage.anaconda.com/anaconda-toolbox-brings-ai-assistant-no-code-development-to-python-in-excel): Microsoft Excel add-in brings AI-powered Anaconda Assistant, curated data catalogs, and cloud features to Python in Excel users. - [Anaconda’s AI Assistant Comes to the Desktop](https://10.2.107.56:8443/blog/anacondas-ai-assistant-comes-to-the-desktop): You can use Anaconda Assistant, our AI-powered chat interface, while coding on your local machine. Learn more in this article. - [Analyzing Time Series Data with Python in Excel](https://10.2.107.56:8443/blog/analyzing-time-series-data-with-python-in-excel): In this blog post, we will explore how the new Python in Excel feature enables a completely new way to work with time series data in Excel. - [Agents, Governance, and Truth: The AI Imperatives of 2025](https://10.2.107.56:8443/blog/anaconda-ai-gartner-report): AI isn’t just transforming industries—it’s reshaping the global competitive landscape and introducing risks that demand immediate action. As enterprises scale... - [12 Reasons to Choose Conda](https://10.2.107.56:8443/blog/12-reasons-to-choose-conda): Conda is a popular package and environment management system that offers a range of benefits compared to other solutions. Here’s why you might choose conda over other options. - [Anaconda Learning: Turbocharge your Python Journey in Anaconda Notebooks](https://10.2.107.56:8443/blog/anaconda-learning-turbocharge-your-python-journey-in-anaconda-notebooks): We have updated the Anaconda Learning library so you can search and access the most recent and best courses using Anaconda Notebooks. - [Anaconda & NVIDIA Enable Seamless GPU Integration for Jupyter Notebooks to Accelerate AI Development](https://stage.anaconda.com/anaconda-nvidia-enable-seamless-gpu-integration-for-jupyter-notebooks): The strategic partnership empowers enterprises to create secure and scalable generative AI applications with accelerated computing capabilities and simplified workflows.... - [2025 AI Predictions: Webinar Recap What's Next in AI?](https://10.2.107.56:8443/blog/anaconda-ai-predictions-2025): Anaconda recently brought together some of the brightest minds in AI for our 2025 AI Predictions Webinar. The panel featured:... - [Address the Need for Python in Generative AI with IBM watsonx.ai and Anaconda](https://10.2.107.56:8443/blog/address-the-need-for-python-in-generative-ai-with-ibm-watsonx-ai-and-anaconda): IBM and Anaconda have collaborated to provide an enterprise-grade generative AI solution that is natively built within IBM watsonx.ai. - [Bringing Python to iOS and Android with BeeWare](https://10.2.107.56:8443/blog/beeware-mobile-python): Twenty years ago, “computing” meant a desktop box or laptop. Today, your smartphone is likely the most powerful computer you... - [Exploring the Latent Space of Programming Styles: How Persona Prompting Unlocks Hidden AI Capabilities](https://10.2.107.56:8443/blog/persona-programming-ai): There are no solved problems; there are only problems that are not yet solved. ” – John Carmack At Anaconda,... - [Introducing Lumen AI: The Open-Source AI Tool That Puts Your Data to Work](https://10.2.107.56:8443/blog/anaconda-launches-lumen-ai): AI is transforming data science, but for many teams, the challenge isn’t just having access to AI—it’s making it work... - [Self-Service Open Data Science: Custom Anaconda Parcels for Cloudera CDH](https://10.2.107.56:8443/blog/self-service-open-data-science-custom-anaconda-parcels-for-cloudera-cdh): This post refers to Anaconda Enterprise 4. To generate custom parcels in Anaconda Enterprise 5, see here . Earlier this year, as part of our partnership… - [Useful Sites for Finding Datasets](https://stage.anaconda.com/useful-sites-for-finding-datasets): Discover 5 lesser-known sites for finding open datasets: Google Dataset Search, OpenML, FiveThirtyEight, and more. Perfect for ML and data science projects. - [The Power of Local Data Science and AI with Anaconda and Lenovo Workstations](https://10.2.107.56:8443/blog/the-power-of-local-data-science-and-ai-with-anaconda-and-lenovo-workstations): Why Work Locally? In a world of ever-growing AI models with hundreds of billions of parameters, a lot of industry... - [Introducing Anaconda for Education: Empowering the Academic Community](https://10.2.107.56:8443/blog/anaconda-for-education-empowering-the-academic-community): At Anaconda, we believe in empowering educators, students, and researchers to achieve their full potential in data science and machine... - [Using Copilot in Excel with Python](https://10.2.107.56:8443/blog/copilot-in-excel-with-python): For business professionals and analysts, Excel has long been the go-to tool for handling data. Yet, its limitations often require... - [Building a Panel Dashboard with Snowpark for Python: Snowflake for Data Scientists](https://stage.anaconda.com/panel-dashboard-with-snowpark): Data scientists often use SQL to interact with a data warehouse, but then often rely on Python for data discovery,... - [New Release: Miniconda 25.1.1](https://10.2.107.56:8443/blog/miniconda-25-1-1-release): We are excited to announce the 25. 1. 1 release of Miniconda, Anaconda’s miniature version of the Anaconda Distribution installer.... - [How to Build Your Own Panel AI Chatbots](https://10.2.107.56:8443/blog/how-to-build-your-own-panel-ai-chatbots): With its latest version 1. 3, the open-source project Panel has just introduced an exciting and highly anticipated new feature:... - [How to Build a Retrieval-Augmented Generation Chatbot](https://10.2.107.56:8443/blog/how-to-build-a-retrieval-augmented-generation-chatbot): Retrieval-augmented generation (RAG) has been empowering conversational AI by allowing models to access and leverage external knowledge bases. In this... - [Python, Panels, and Progress: Running into 2025](https://10.2.107.56:8443/blog/learn-panel-python-in-2025): Training for the Boston Marathon requires consistency, commitment, and a way to track progress over time. Imagine having a custom-built... - [Anaconda Package Download Data: Updates and Fixes for More Accurate Statistics](https://10.2.107.56:8443/blog/package-download-data-updates-fixes-for-accurate-statistics): TL;DR: We’ve rolled out major improvements to our package download statistics, including more accurate download counts and better tracking of... - [Python in Excel for the Supply Chain and Manufacturing Industry](https://10.2.107.56:8443/blog/python-in-excel-for-supply-chain-and-manufacturing): For years, Excel has been the essential tool for operations managers and analysts, providing a powerful platform for managing data,... - [State of Data Science: AI and Open Source at Work](https://stage.anaconda.com/state-of-data-science-2024-key-findings): For the seventh consecutive year, we conducted our State of Data Science survey to surface insights about the demographics of... - [Level Up Your Snowflake Notebook with Enterprise Python Analytics](https://10.2.107.56:8443/blog/level-up-your-snowflake-notebook-with-enterprise-python-analytics): Snowflake Notebooks, launched in summer 2024, provides a convenient and easy-to-use environment for Python, SQL, and Markdown using a cell-based... - [Python in Excel for the Retail and ECommerce Industry](https://10.2.107.56:8443/blog/python-in-excel-for-retail-and-ecommerce): For retail and e-commerce companies, leveraging customer data effectively is crucial to improving satisfaction, enhancing retention, and increasing sales. However,... - [New Release: Miniconda 24.11.1](https://stage.anaconda.com/new-release-miniconda-24-11-1): We are excited to announce the 24. 11. 1 release of Miniconda, Anaconda’s miniature version of the Anaconda Distribution installer.... - [Synthetic Data: The New Fuel for AI’s Rapid Evolution](https://10.2.107.56:8443/blog/synthetic-data-the-new-fuel-for-ais-rapid-evolution): For decades, AI has relied on real-world data as its backbone, fueling everything from predictive text to autonomous vehicles. However,... - [Master Python in Excel with Anaconda’s Python in Excel Cheat Sheet](https://10.2.107.56:8443/blog/python-in-excel-cheat-sheet): Python in Excel opens a world of possibilities, from advanced data analysis to crafting stunning visualizations—all without leaving your spreadsheet.... - [Introducing Enhanced Usage Insights for Anaconda Packages](https://10.2.107.56:8443/blog/introducing-enhanced-usage-insights-for-anaconda-packages): At Anaconda, we continually strive to improve the user experience for our community and customers. To serve our community better,... - [Unpacking Holiday Travel Trends](https://10.2.107.56:8443/blog/unpacking-holiday-travel-trends): The travel industry is constantly changing, shaped by new technology, economic shifts, and global events. Tools like Google Flights, shifts... - [Open-Source AI in the Enterprise: Insights from a Survey of IT Leaders](https://10.2.107.56:8443/blog/anaconda-state-of-enterprise-open-source-ai): AI and machine learning have become the engines of enterprise innovation, and open-source tools are the fuel. Whether it’s speeding... - [Anaconda Available in the AWS Marketplace](https://10.2.107.56:8443/blog/anaconda-available-in-aws-marketplace): We are excited to announce that Anaconda is available in the AWS Marketplace. The partnership will allow AWS users to... - [Serving Up Holiday Code with AI Navigator This Thanksgiving](https://10.2.107.56:8443/blog/serve-holiday-code-ai-navigator-anaconda): With Thanksgiving upon us, we decided to put together a fun project that showcases how you can use generative AI... - [November’s Numbers: U.S. Political Trends, Visualized](https://10.2.107.56:8443/blog/analyze-u-s-election-data-python-anaconda): The 2024 U. S. presidential election offers a rich dataset for data scientists to explore. Using the open-source Python libraries... - [New Release: Miniconda 24.9.2](https://stage.anaconda.com/new-release-miniconda-24-9-2): We are excited to announce the 24. 9. 2 release of Miniconda, Anaconda’s miniature version of the Anaconda Distribution installer,... - [Anaconda Toolbox Now Generally Available: Bringing Enterprise-Grade Python Analytics to Excel](https://10.2.107.56:8443/blog/anaconda-toolbox-bringing-enterprise-grade-python-analytics): We are thrilled to announce the General Availability of Anaconda Toolbox for Excel! Anaconda Toolbox for Excel is our comprehensive... - [Anaconda Code: Create and Use User Defined Functions](https://10.2.107.56:8443/blog/anaconda-code-create-user-defined-functions): We’re excited to announce a powerful new feature in Anaconda Code: Python User-Defined Functions! With UDFs, you can write Python... - [Why Python? 10 Reasons Behind Its Popularity](https://10.2.107.56:8443/blog/why-python): Discover why Python became the #1 programming language. Learn its history, use cases, and why 47M developers choose it for AI and data science. - [VBA vs Python in Excel – What You Need to Know](https://10.2.107.56:8443/blog/vba-vs-python-in-excel): If you’re looking to improve your data analysis skills and are unsure whether you should learn Visual Basic for Applications (VBA) or Python in Excel, you’ll want to read this! - [Building Data Science Solutions with Anaconda](https://10.2.107.56:8443/blog/dan-meador-free-book-chapter): Building Data Science Solutions with Anaconda is a comprehensive starter guide to building robust and complete models. The book covers... - [Which packages should I install?](https://10.2.107.56:8443/blog/how-to-pick-packages): To help you find the packages and projects best suited to your needs, Anaconda provides a categorized view of the most popular and well-maintained packages built for performance, security, and more. - [Product Update: Anaconda CLI](https://stage.anaconda.com/product-update-anaconda-cli): Anaconda has updated the CLI from a monolith and a plugin architecture. - [The Power of Code Snippets: Enhancing Productivity in Data Science](https://10.2.107.56:8443/blog/the-power-of-code-snippets): Code snippets, available through Anaconda Toolbox, allow time savings and more efficient workflows in Notebooks. Try it today! - [New Release: Anaconda Distribution 2024.10](https://10.2.107.56:8443/blog/new-release-anaconda-distribution-2024-10): We are excited to announce the 2024. 10 release of Anaconda Distribution, Anaconda’s data science distribution installer, which includes: Python... - [Halloween AI Tricks, Treats, & Stats, Visualized](https://10.2.107.56:8443/blog/analyze-halloween-data-ai-python): For Halloween this year, we decided to look into what consumer economic data can tell us about how Americans celebrate... - [New environment management capabilities in public beta on Package Security Manager](https://stage.anaconda.com/new-environment-management-capabilities-in-public-beta-on-package-security-manager): We are excited to introduce the environment management feature on Package Security Manager (PSM) on Cloud! This new environment management... - [Python in Excel for Marketing](https://10.2.107.56:8443/blog/python-in-excel-for-marketing): Learn how Marketing teams can take advantage of Python in Excel's advanced data analysis capabilities. - [Mastering Prompt Engineering: Unlocking the Power of LLMs](https://10.2.107.56:8443/blog/mastering-prompt-engineering-llms): In the rapidly evolving field of artificial intelligence, prompt engineering has emerged as a crucial skill for developers, data scientists,... - [Implementing Policies at the Channel Level on Package Security Manager](https://stage.anaconda.com/implementing-policies-at-the-channel-level-on-package-security-manager): Implementing channel-level policies in Anaconda’s Package Security Manager will help streamline your package management process and keep your organization's repositories secure against vulnerabilities. - [Python 3.8 Reaches End-Of-Life](https://10.2.107.56:8443/blog/python-3-8-reaches-end-of-life): As part of our commitment to maintaining up-to-date and secure Python environments, Anaconda is announcing the end of support for... - [A Comprehensive List of Python in Excel Resources](https://10.2.107.56:8443/blog/a-comprehensive-list-of-python-in-excel-resources): Are you new to Python in Excel or looking to take your skills to the next level? We’ve created this... - [Python in Excel for Finance](https://10.2.107.56:8443/blog/python-in-excel-for-finance): Traditionally, finance professionals have relied heavily on Microsoft Excel as a go-to tool for tasks like financial modeling, budgeting, forecasting,... - [Update on Anaconda’s Terms of Service for Academia and Research](https://10.2.107.56:8443/blog/update-on-anacondas-terms-of-service-for-academia-and-research): Please note this post is outdated. Our current Terms of Service policies can be reviewed here. Recently, our users at... - [Anaconda Toolbox Makes Python in Excel as Easy as =PY()](https://10.2.107.56:8443/blog/anaconda-anaconda-toolbox-makes-python-in-excel-as-easy-as-py): We’re excited to announce new tools in our Anaconda Toolbox! These new tools, as well as existing ones, were created... - [Introducing Anaconda Code add-in for Microsoft Excel](https://10.2.107.56:8443/blog/introducing-anaconda-code-add-in-for-microsoft-excel): Excel and Python users can now run their Python-powered projects in Excel locally with the Anaconda Code add-in “I wish... - [Analyzing Olympic Game Data with Anaconda](https://10.2.107.56:8443/blog/analyzing-olympic-game-data-anaconda): Olympics as Charts The Olympic Games have a rich history dating back to ancient Greece, but the modern Olympics, as... - [Anaconda Accelerates AI Development and Deployment with NVIDIA CUDA Toolkit](https://10.2.107.56:8443/blog/anaconda-accelerates-ai-development-and-deployment-with-nvidia-cuda-toolkit): We are pleased to announce that NVIDIA CUDA Toolkit 12 is now available on our main (AKA, defaults) channel, a... - [Introducing Evaluations Driven Development: Building AI Assistants That Deliver Real Value](https://10.2.107.56:8443/blog/introducing-evaluations-driven-development): At Anaconda, we’ve developed a rigorous new approach to AI development called Evaluations Driven Development (EDD). By continuously testing AI... - [Introducing AI Navigator: Your Secure Desktop Gateway to Generative AI](https://10.2.107.56:8443/blog/introducing-anaconda-ai-navigator): Discover AI Navigator, Anaconda’s new desktop application for accessing and experimenting with over 200 local LLMs. Join our public beta and download AI Navigator today! - [New Release: Anaconda Distribution 2024.06](https://10.2.107.56:8443/blog/new-release-anaconda-distribution-2024-06): The latest release of Anaconda Distribution, Anaconda’s free data science distribution installer, is now live. - [Accelerating AI Development and Deployment with Lenovo](https://10.2.107.56:8443/blog/accelerating_ai_development_and_deployment_with_lenovo): In an exciting move, Lenovo, the world’s #1 PC and #1 Top500 supercomputer manufacturer and Anaconda announce a strategic partnership... - [Anaconda Recognized in insideBIGDATA's IMPACT 50 List](https://10.2.107.56:8443/blog/anaconda_recognized_in_insidebigdatas_impact_50_list): We’re thrilled to announce that Anaconda has been named one of the most impactful companies in the industry by insideBIGDATA,... - [Anonymous Usage Data Collection in Miniconda](https://10.2.107.56:8443/blog/anonymous-usage-data-collection-in-miniconda): Last September, we announced the inclusion of anaconda-anon-usage in two of our signature products: Anaconda Distribution and Anaconda Navigator. That... - [Anaconda Not Affected by Malicious xz Code](https://10.2.107.56:8443/blog/anaconda-not-effected-by-malicious-xy-code): What Happened? A March 29, 2024 announcement brought to light malicious code that affects the latest version of the “xz”... - [Visual Data Analysis with Python in Excel: Using Bar Charts](https://10.2.107.56:8443/blog/visual-data-analysis-with-python-in-excel-using-bar-charts): This is the fourth in a series of blog posts that teach you to analyze data using Python code in... - [Visual Data Analysis with Python in Excel: Using Line Charts](https://10.2.107.56:8443/blog/visual-data-analysis-with-python-in-excel-using-line-charts): This is the fifth and final in a series of blog posts that teach you to analyze data using Python... - [Visual Data Analysis with Python in Excel: Using Histograms](https://10.2.107.56:8443/blog/visual-data-analysis-with-python-in-excel-using-histograms): Many types of professionals analyze data using different skills. Microsoft Excel users analyze data using Excel PivotTables, while data scientists... --- ## News - [Anaconda Raises Over $150M in Series C Funding to Power AI for the Enterprise](https://stage.anaconda.com/newsroom/anaconda-raises-150m-series-c-funding-ai-enterprise): With Insight Partners-led Round, Anaconda Establishes Role as the Standardized Python Distribution for Mission-Critical AI Systems AUSTIN, TX – –... - [Anaconda Announces Partnership with Prefix.dev to Bring  Next-Generation Functionality to conda-build](https://10.2.107.56:8443/press/announcing-strategic-enhancement-to-conda-build): Empowering builders and enterprises with 3-5x faster package creation while strengthening the entire open source ecosystem AUSTIN, TX (July 9,... - [Anaconda Partners with Databricks to Bridge Security and Governance Gaps in Enterprise AI Development](https://stage.anaconda.com/press/anaconda-partners-with-databricks-bridge-security-and-governance-gaps-enterprise-ai-development): Open Source Champions Join Forces to Accelerate Enterprise AI Adoption with Streamlined Data Science Workflows and Enhanced Security and Compliance... - [Anaconda Unveils the First Unified AI Platform for Open Source](https://10.2.107.56:8443/press/introducing-the-anaconda-ai-platform): A Total Economic Impact Study Reveals Organizations Building AI with Anaconda Experience 80% Improvement in Operational Efficiency and 60% Reduced... - [Anaconda Named to Fast Company’s Annual List of the World’s Most Innovative Companies](https://10.2.107.56:8443/press/anaconda-named-to-fast-company-most-innovative-companies-list): Recognition validates pervasiveness of Python as foundational to advancement of AI as enterprise adoption soars AUSTIN, TX (March 18, 2025)... - [AI Shortfalls and Security Risks Demand Open-Source Collaboration, Anaconda Finds in State of Data Science Report ](https://10.2.107.56:8443/press/ai-shortfalls-and-security-risks-demand-open-source-collaboration-anaconda-finds-in-state-of-data-science-report): Seventh annual survey of data science professionals shows 87% are using AI as much or more than last year, but... - [Anaconda and AWS Unlock New Possibilities for Enterprise AI Development](https://stage.anaconda.com/newsroom/anaconda-and-aws-unlock-new-possibilities-for-enterprise-ai-development): Anaconda Business is now available in the AWS Marketplace, equipping users with a robust ecosystem of secure open-source tools and libraries. - [Beyond Moonshots: AI Agents Will Come To Earth In 2025](https://10.2.107.56:8443/newsroom/beyond-moonshots-ai-agents-will-come-to-earth-in-2025) - [Anaconda Unites Teams Across Data Skill Levels With Anaconda Toolbox for Excel ](https://10.2.107.56:8443/press/anaconda-toolbox-general-availability): Anaconda releases Anaconda Toolbox into general availability, equipping Excel users with Python-powered data analysis and collaboration tools. - [Agents Of Change: How AI-API Synergy Turns Insights Into Action](https://10.2.107.56:8443/newsroom/agents-of-change-how-ai-api-synergy-turns-insights-into-action) - [Anaconda brings the power of large language models to laptops](https://10.2.107.56:8443/newsroom/anaconda-brings-the-power-of-large-language-models-to-laptops) - [Anaconda Brings Generative AI Models to Desktops withLaunch of AI Navigator](https://10.2.107.56:8443/press/anaconda-ai-navigator-generative-ai-desktop-agent): Anaconda's AI Navigator is now generally available to all users. Build AI agents with the latest generative AI models on your desktop. - [Anaconda Announces General Availability of Python in Excel, Transforming Data Analysis for Millions of Excel Users](https://10.2.107.56:8443/press/anaconda-microsoft-python-in-excel-general-availability): Following a successful Public Preview, Python in Excel makes secure Python-powered data analysis and machine learning possible. AUSTIN, TX —... - [Anaconda Announces General Availability of Python in Excel, Transforming Data Analysis for Millions of Excel Users](https://10.2.107.56:8443/press/anaconda-microsoft-python-in-excel-general-availability): Following a successful Public Preview, Python in Excel makes secure Python-powered data analysis and machine learning possible. AUSTIN, TX —... - [Anaconda's chief AI and innovation officer says Python can make on-premises IT solutions easier and safer](https://10.2.107.56:8443/newsroom/anacondas-chief-ai-and-innovation-officer-says-python-can-make-on-premises-it-solutions-easier-and-safer) - [Anaconda Debuts New Solution to Run Python Locally in Microsoft Excel  ](https://stage.anaconda.com/newsroom/anaconda-code-toolbox-python-in-excel): The new Anaconda Code via the Anaconda Toolbox for Excel allows users to run Python code locally within their workbooks. - [Anaconda Launches Snowflake Notebooks Integration to Further Democratize Python for Data Science, AI Use Cases ](https://10.2.107.56:8443/press/anaconda-snowflake-notebooks-integration): Anaconda integrates its secure repository of Python and R packages into Snowflake Notebooks to accelerate data science and AI projects. - [The 10 Hottest Data Science And Machine Learning Tools Of 2024](https://10.2.107.56:8443/newsroom/the-10-hottest-data-science-and-machine-learning-tools-of-2024) - [Anaconda and Teradata Partner to Enhance Open-Source Support for Trusted AI Innovation in Teradata VantageCloud ](https://10.2.107.56:8443/press/anaconda-teradata-partner-open-source-ai): Integrating Anaconda's secure Python and R packages with Teradata's ClearScape Analytics aims to accelerate data science and generative AI applications. - [Anaconda Collaborates with IBM to Provide Python in Generative AI with IBM watsonx.ai](https://10.2.107.56:8443/press/anaconda-partners-with-ibm-watsonx-to-deliver-enterprise-scale-ai-solutions): IBM watsonx.ai users can access Anaconda’s open-source software repository and have the option to upgrade to the premium repository. - [Anaconda Joins IBM, Meta, and More in AI Alliance to Advance Open-Source AI Adoption, Safety, and Accessibility](https://10.2.107.56:8443/press/anaconda-joins-ai-alliance): AI Alliance established two pivotal working groups focused on AI Safety and Trust Tooling and AI Policy Advocacy. Anaconda participates in both groups - [New Anaconda Leadership and AI Incubator Will Drive Value for All of Open-Source AI](https://10.2.107.56:8443/press/anaconda-leadership-ai-incubator): Anaconda seamlessly integrates Python into Microsoft Excel's grid to extend data science, artificial intelligence, and machine learning capabilities to all Excel users. - [How the Cloud Is Changing Data Science](https://10.2.107.56:8443/newsroom/how-the-cloud-is-changing-data-science) - [Anaconda’s Sixth Annual State of Data Science Report Report Reveals Surge in AI Upskilling Among Data and IT Professionals](https://10.2.107.56:8443/press/anaconda-state-of-data-science-2023): Anaconda's 2023 State of Data Science report highlights how generative AI creates new talent needs, fears, and opportunities in the field. - [Anaconda Distribution for Python Brings Data Science to Hundreds of Millions of Microsoft Excel Users](https://10.2.107.56:8443/press/anaconda-distribution-for-python-brings-data-science-to-hundreds-of-millions-of-microsoft-excel-users): Anaconda has integrated Python into Microsoft Excel’s grid to extend data science, artificial intelligence, and machine learning capabilities to Excel users. - [Anaconda Supports Pandata, the Scalable Open-Source Analysis Stack for High-Powered Scientific Data Analysis](https://10.2.107.56:8443/newsroom/anaconda-supports-scalable-open-source-analysis-sosa-stack-for-high-powered-scientific-data-analysis): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Anaconda Launches Conda Fundamentals Certification ](https://10.2.107.56:8443/press/anaconda-launches-conda-fundamentals-certification): The first-ever conda certification program aims to help learners unlock the ability to package and distribute software. - [Anaconda Acquires EduBlocks to Empower K-12 Data Literacy and Expand Educational Offerings](https://10.2.107.56:8443/press/anaconda-acquires-edublocks-to-empower-k-12-data-literacy-and-expand-educational-offerings): \Anaconda expands its core SaaS offerings to provide accessible, cloud-based resources for Python development at all skill levels. - [Anaconda Debuts Data Science Expo to Encourage Student Data Literacy](https://10.2.107.56:8443/press/anaconda-debuts-data-science-expo-to-encourage-student-data-literacy): The competition-based expo will host three regional events in 2023 designed to provide a comprehensive and interactive learning experience AUSTIN,... - [Anaconda Launches New Channel Partner Program](https://10.2.107.56:8443/press/anaconda-launches-new-channel-partner-program): AUSTIN, TX – April 4, 2023 – Anaconda Inc., provider of the world’s most popular platform to develop and deploy secure Python solutions, today announced a new channel partner program for Value-Added Resellers (VARs), Distributors, Direct Market Resellers (DMRs), Global System Integrators (GSIs), and Referral Partners worldwide to accelerate Python and open-source package deployments to the enterprise. - [Anaconda Launches PyScript.com, Democratizes Python for All](https://10.2.107.56:8443/press/anaconda-launches-pyscriptcom-democratizes-python-for-all): The revolutionary platform enables programming for the 99%, advancing Anaconda’s mission to democratize data science and Python development. AUSTIN, TX... - [Anaconda and Domino Data Lab to Deliver Integrated AI/ML Lifecycle Support for Python and R Users](https://10.2.107.56:8443/press/anaconda-and-domino-data-lab-to-deliver-integrated-ai-ml-lifecycle-support-for-python-and-r-users): New partnership adds access to the complete Anaconda repository within Domino’s Enterprise MLOps platform for faster time-to-value AUSTIN, Texas, November,... - [Anaconda and Snowflake Announce General Availability of Snowpark for Python Integration](https://10.2.107.56:8443/press/anaconda-and-snowflake-announce-general-availability-of-snowpark-for-python-integration): The native integration brings Anaconda to Snowflake Data Cloud users AUSTIN, TX – November 7, 2022 – Anaconda Inc. ,... - [Anaconda Research Finds Majority of Organizations Scaled Back Their Open-Source Software Usage Due to Security Fears](https://10.2.107.56:8443/press/anaconda-research-finds-majority-of-organizations-scaled-back-their-open-source-software-usage-due-to-security-fears): Rising security concerns, limited talent, and ethical dilemmas are seen as the biggest threats to the future of data science... - [Anaconda Announces Strategic Cloud Partnership with Oracle to Enable Seamless, Secure Open-Source Innovation in the Cloud](https://10.2.107.56:8443/press/anaconda-oracle-partnership): Anaconda and Oracle partner to help secure the open-source pipeline in high-performance machine learning on Oracle Cloud Infrastructure (OCI) AUSTIN,... - [Anaconda Acquires PythonAnywhere to Expand Python Team Collaboration in the Cloud](https://10.2.107.56:8443/press/anaconda-acquires-pythonanywhere): The acquisition adds capabilities designed to unite teams and create access to more robust cloud resources. AUSTIN, Texas–(BUSINESS WIRE)–Anaconda Inc.... - [Anaconda’s Embedded Repository and Package Manager in Snowpark for Python to Enter Public Preview](https://10.2.107.56:8443/press/anacondas-embedded-repository-and-package-manager-in-snowpark-for-python-to-enter-public-preview): The native integration brings Anaconda, the world’s most popular data science platform, to Snowflake Data Cloud users seeking enterprise-grade Python... - [Anaconda Announces Collaboration with Esri, Setting Enterprise Standard for Python Across the Geospatial Community](https://10.2.107.56:8443/newsroom/anaconda-announces-collaboration-with-esri-setting-the-enterprise-standard-for-python-across-the-geospatial-community): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Anaconda Welcomes Python Thought Leaders to Advance Technical Frontier](https://10.2.107.56:8443/newsroom/anaconda-welcomes-python-thought-leaders-to-advance-technical-frontier): Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - [Anaconda Launches Embedded Partner Program as Demand for Python Continues to Soar](https://10.2.107.56:8443/press/anaconda-launches-embedded-partner-program-as-demand-for-python-continues-to-soar): The program will enable organizations to seamlessly and securely integrate open-source Python into their own products, spurring innovation across industries... - [DataCamp Partners with Anaconda to Enable Data Fluency for Teams](https://10.2.107.56:8443/press/datacamp-partners-with-anaconda-to-enable-data-fluency-for-teams): Anaconda Team Edition customers now have access to DataCamp training content NEW YORK, June 17, 2020 /PRNewswire/ — DataCamp, the... --- ## Resources - [Test Infographic Resource](https://stage.anaconda.com/resource/test-infographic-resource) - [State of Data Science Report 2024](https://stage.anaconda.com/resource/state-of-data-science-report-2024): Major Benefits 0 % of respondents ranked “most useful, most economical, and speed” as a top 3 value of open-source... - [Entercard Accelerates Credit Risk Modeling with Anaconda and Snowflake](https://stage.anaconda.com/resources/case-study/entercard-accelerates-credit-risk-modeling-with-anaconda-and-snowflake): Entercard Accelerates Credit Risk Modeling with Anaconda and Snowflake How a leading Nordic credit card company reduced model development time... - [Close Your AI Model Governance Gap](https://stage.anaconda.com/resources/report/bridging-the-ai-model-governance-gap): Discover why 67% of organizations face AI deployment delays due to security concerns. Download our exclusive survey of 300+ AI leaders revealing critical governance gaps and solutions. - [Making Enterprise AI Work](https://stage.anaconda.com/webinar/making-enterprise-ai-work/): Enterprise AI projects are failing at alarming rates. Despite massive investments, most organizations struggle to move from proof-of-concept to production-scale... - [Enterprise ML](https://stage.anaconda.com/resources/report/state-of-data-science-2019): Get the latest state of data science report from Anaconda. See the latest trends. - [Data Science Security](https://10.2.107.56:8443/resources/guide/how-to-implement-an-oss-governance-program-for-data-science-security): Learn what to watch out for when downloading open-source packages and how to establish a governance program to get more models into production. - [Open Source Security By the Numbers](https://stage.anaconda.com/resource/open-source-security-by-the-numbers) - [Open source security by the numbers 2023](https://10.2.107.56:8443/resources/report/open-source-security-by-the-numbers-2023) - [Paying Dividends 2021-2022 Contributions to the open source community](https://10.2.107.56:8443/resources/report/paying-dividends-2021-2022-contributions-to-the-open-source-community) - [Forrester Study The Total Economic Impact of Anaconda ](https://10.2.107.56:8443/resources/report/forrester-tei-impact-of-anaconda): Discover how Anaconda delivers 119% ROI over three years with significant security and time savings. Download the Forrester TEI study now to boost your organization's efficiency. - [Conda: A Package Manager for Data Science, ML, and AI](https://10.2.107.56:8443/guides/conda-package-manager-for-data-sciences-ml-and-ai): Discover how Conda simplifies package management for data science, ML, and AI. Learn why it's the easiest way to set up a functional Python environment. - [Python Packages: Installation & Management Best Practices](https://10.2.107.56:8443/guides/python-packages): Learn how Python packages work and how to manage them, plus essential tools for open-source AI and data science. - [The Women Illuminating the Path to AI Success](https://10.2.107.56:8443/resources/webinar/women-illuminating-path-ai-success): Unlock AI success implementation with Forrester’s Brandon Purcell and women tech leaders. Learn game-changing AI strategies. Watch on-demand now! - [PNC Financial Services](https://10.2.107.56:8443/resources/case-study/pnc-financial-services): PNC ditched proprietary tools for Anaconda Enterprise, empowering teams bank-wide with open-source Python and cutting processing time by 90%. - [2025 AI Predictions: Uncover the Future of AI with Industry Experts](https://10.2.107.56:8443/resources/webinar/2025-ai-predictions): What’s Next in AI? Register to access our exclusive on-demand webinar, 2025 AI Predictions Webinar, where top experts from Anaconda... - [8 Best Machine Learning Software To Use in 2025](https://10.2.107.56:8443/guides/machine-learning-software): Discover the top machine learning open-source software to boost your AI and data science projects. - [Exploring Open-Source AI: Definition, Benefits, and Tools](https://stage.anaconda.com/topics/open-source-ai): The artificial intelligence landscape is rapidly evolving, with open source AI emerging as a transformative force that’s democratizing access to... - [Top Agentic AI Tools and Frameworks for 2025](https://10.2.107.56:8443/guides/agentic-ai-tools): Discover the top agentic AI tools, key features to look for, and best practices for developing and deploying AI agents for your organization. - [7 Must-Know Machine Learning Libraries in 2025](https://stage.anaconda.com/guides/machine-learning-libraries): Machine learning (ML) is revolutionizing industries, driving advancements in healthcare, finance, retail, and beyond. From personalized recommendations to fraud detection, ML... - [What Are AI Agents and How Do They Work?](https://stage.anaconda.com/topics/what-are-ai-agents): As artificial intelligence transforms industries worldwide, understanding AI agents—their types, functions, and real-world applications—has become essential for leveraging their full... - [Why You Need Machine Learning](https://stage.anaconda.com/resources/whitepaper/why-you-need-ml): AI is poised to make a huge economic and reshape the competitive landscape in every major industry. To realize this value, you must operationalize machine learning. - [The Complete Data Science Platform Buyer’s Guide](https://10.2.107.56:8443/resources/whitepaper/the-complete-data-science-platform-buyers-guide): Choose the right platform to accelerate your ML life cycle with an overview of key considerations and checklist of infrastructure, security, and integration features. - [Guide to Open-Source Tools & Libraries for Data Science and Machine Learning](https://stage.anaconda.com/resources/whitepaper/os-tools): Open Source Tools & Libraries - [How do I become a data scientist? ](https://10.2.107.56:8443/resources/whitepaper/how-do-i-become-a-data-scientist): Learn about what it takes to become a data scientist. What you should learn in school and what kind of additional skills you will need to succeed. Learn more. - [Getting Started with Deep Learning in the Enterprise](https://stage.anaconda.com/resources/whitepaper/deep-learning): Deep Learning - [How to Implement an OSS Governance Program for Data Science, Artificial Intelligence, and Machine Learning](https://stage.anaconda.com/resources/whitepaper/data-science-security): How to Implement an OSS Governance Program for Data Science, Artificial Intelligence, and Machine Learning - [Building Data Science Solutions with Anaconda](https://10.2.107.56:8443/resources/whitepaper/dan-meador-free-book-chapter): About this report Building Data Science Solutions with Anaconda is a comprehensive starter guide to building robust and complete models.... - [Selecting an Enterprise Platform for Python and Open Source: A Checklist for Buyers](https://10.2.107.56:8443/guides/selecting-an-enterprise-platform-for-python-and-open-source-a-checklist-for-buyers): Leveraging the power of open-source software across an enterprise organization requires capabilities for building and deploying secure Python solutions. This guide explores what to consider when choosing an enterprise platform to collaborate and build powerful applications with data science and machine learning. - [Leveraging AI for Enterprise Success: Insights for Leaders](https://10.2.107.56:8443/guides/leveraging-ai-for-enterprise-success): AI offers unprecedented opportunities for enterprise organizations. We created this guide to be a handy reference about opportunities, challenges, and best practices of AI for enterprise. - [Determine the Right DS Platform](https://stage.anaconda.com/resources/whitepaper/build-vs-buy): Get the key considerations for evaluating a data science platform (in-house or external) and determine the right platform for your company, in this whitepaper from Anaconda. - [Enterprise Machine Learning](https://stage.anaconda.com/resources/whitepaper/enterprise-ml): Creating Context for Data Scientist and Developer Collaboration n this report, we drill down into the developer and data scientist... - [The Definitive Guide to AI Platforms for Open-Source Data Science and ML](https://10.2.107.56:8443/guides/definitive-guide-to-ai-platforms): Explore top open-source tools and use cases across industries. Act strategically to empower practitioners and teams to build, deploy, and maintain secure AI solutions. - [Tech Talk: CAIO Unplugged](https://10.2.107.56:8443/resources/webinar/tech-talk-caio-unplugged): Join us for an exclusive Tech Talk with Anaconda’s Chief AI Officer, Peter Wang! In this interactive Q&A session. - [Data Science vs Data Analytics: What’s the Difference?](https://10.2.107.56:8443/topics/data-science-vs-data-analytics): Discover the key differences between data science and data analytics, understand their unique applications, and learn how AI is transforming both fields. - [Comparing Data Science and AI: Where They Overlap and Differ](https://10.2.107.56:8443/topics/data-science-and-ai): Understand the difference between data science and AI, and explore real-world examples of how they complement each other. - [Automate Your Analysis with Snowflake and Anaconda](https://10.2.107.56:8443/resources/webinar/automate-analysis-snowflake-anaconda): About This Webinar Learn the power of Snowflake SQL and Anaconda Python packages to simplify tasks that often require advanced... - [State of Data Science 2024 Report](https://10.2.107.56:8443/resources/report/state-of-data-science-report-2024): State of Data Science 2024 Report AI and Open Source at Work This 7th annual report reveals insights about the... - [Top 14 Enterprise AI Use Cases in 2025](https://stage.anaconda.com/topics/enterprise-ai-use-cases/): Learn more about how enterprise AI can be applied to various use cases within your organization. - [The Future of Data Science: 8 Trends to Watch](https://10.2.107.56:8443/topics/future-of-data-science): Get Anaconda’s predictions for what the next 10 years of data science will bring. Keyword: future of data science - [Deploying Machine Learning to Apple Devices with coremltools](https://10.2.107.56:8443/resources/webinar/apple-coremltools-lp): About this Webinar Machine learning has gone beyond the data center and is now being deployed to the devices we... - [Graph Analytics for Data Scientists](https://10.2.107.56:8443/resources/webinar/graph-analytics-for-data-scientists): Analyzing data using conventional statistical methods involves looking at tabular data where data points are independent of each other, e.... - [Building an OSS Governance Program for ML in the Enterprise](https://10.2.107.56:8443/resources/webinar/building-an-oss-governance-program-for-ml-in-the-enterprise): About this Webinar Open-source software (OSS) is at the heart of many of the innovations in machine learning (ML) due... - [Secure by Design: How Conda Signature Verification Secures Your Software Pipeline from the Start](https://10.2.107.56:8443/resource/secure-by-design-how-conda-signature-verification-secures-your-software-pipeline-from-the-start): About this Webinar In this session, we discuss the content trust features in conda and the Anaconda Professional Repository. We’ll... - [Data-Driven Manufacturing: AI, ML, and Data Science](https://10.2.107.56:8443/resources/webinar/data-driven-manufacturing-ai-ml-and-data-science): About this Webinar AI techniques like machine learning (ML) and deep learning (DL) offer new ways to optimize processes, automate... - [Efficient Data Preparation with Python](https://stage.anaconda.com/resources/whitepaper/efficient-data-prep-with-python): About this report Data discovery and data preparation have always been among the most time-intensive steps of a research project... - [Best AI Development Tools: 2025 Guide](https://10.2.107.56:8443/guides/ai-development-tools): Discover the best AI development tools. Compare model building, training, and deployment tools, and get expert guidance on choosing the right solution. - [Python for Data Science: A Complete Guide](https://10.2.107.56:8443/guides/python-for-data-science): Discover how Python is used for data science and the tools, libraries, and steps used in this process. - [Mastering Python Data Visualization: A Comprehensive Guide ](https://10.2.107.56:8443/guides/python-data-visualization): Discover the essentials of Python data visualization, including top libraries, practical tips for customization, and techniques for impactful visualizations. - [Best Python Frameworks for Data Science, AI, & Web Development](https://10.2.107.56:8443/topics/python-frameworks): Learn about the most popular Python frameworks available for data science, AI, and web development. - [Introduction to Python in Excel](https://10.2.107.56:8443/resources/webinar/introduction-to-python-in-excel): Webinar Description Open-source tools are transforming AI and machine learning (ML) initiatives across industries. From accelerating innovation to addressing pressing... - [Python for Data Analysis: When and How to Use It](https://10.2.107.56:8443/topics/python-for-data-analysis): Learn how Python is used in data analysis and how commonly used Python libraries support the process. - [Anaconda vs. Python: What’s the Difference?](https://10.2.107.56:8443/topics/choosing-between-anaconda-vs-python): Discover the key differences between Anaconda vs Python in our comprehensive guide. Explore their features, use cases, package management, and more. - [9 of the Best Python Machine Learning Tools](https://10.2.107.56:8443/guides/python-machine-learning-tools): Discover the most popular Python machine learning tools used in data science and analytics. - [Top Strategic Technology Trends for 2025](https://10.2.107.56:8443/resources/webinar/top-strategic-technology-trends-for-2025): About Compliments of Anaconda, the Gartner Top Strategic Technology Trends for 2025 report is the roadmap CIOs, CTOs, and IT... - [7 Best MLOps Tools [2025 Buyer’s Guide]](https://10.2.107.56:8443/guides/the-top-mlops-tools): Learn what key features you should look for in every type of MLOps tool and the top tools that every organization should consider. - [Open-Source Security: Risks, Benefits, and Best Practices](https://10.2.107.56:8443/guides/open-source-security): Explore the complexities of open-source security, including risks, benefits, and strategies to safeguard your software supply chain against vulnerabilities. - [Best Python Libraries for Data Science, Machine Learning, & More](https://10.2.107.56:8443/topics/best-python-libraries): Discover the best Python libraries for data science, machine learning, and more. Discover top libraries, their use cases, and practical examples to get started. - [The 5 Best Data Science Platforms in 2024](https://10.2.107.56:8443/guides/data-science-platform-buyers-guide): The 2024 data science platform buyer's guide aims to help readers understand the features, benefits, and key considerations of the top data science platforms. - [Best Data Science Tools in 2024](https://10.2.107.56:8443/guides/data-science-tools): Learn what key features you should look for in every type of data science tool and the top tools that every organization should consider. - [Deep Learning vs Machine Learning: Whats the Difference?](https://10.2.107.56:8443/topics/deep-learning-vs-machine-learning): Explore the key differences between deep learning and machine learning. Understand their applications to choose the right approach for your AI projects. - [Data Science vs Machine Learning: What’s the Difference?](https://10.2.107.56:8443/topics/data-science-vs-machine-learning): Understanding the overlap and differences between data science and machine learning helps you leverage each technique effectively for your organization’s needs. - [The Ultimate Guide to Open-Source Security with Python and R](https://10.2.107.56:8443/guides/the-ultimate-guide-to-open-source-security-with-python-and-r): Learn what to consider when choosing an enterprise platform for building and deploying powerful, secure Python, AI, machine learning, and data science solutions. - [Anaconda + Potsdam Institute for Climate Impact Research PIK](https://10.2.107.56:8443/resources/case-study/anaconda-potsdam-institute-for-climate-impact-research-pik) - [Anaconda + OpenEye Scientific](https://10.2.107.56:8443/resources/case-study-anaconda-openeye-scientific): Learn how Anaconda powers OpenEye Scientific's Orion platform, providing reliable Python package management and seamless access to scientific libraries for cloud-based molecular design and pharmaceutical development. - [2022 State of Data Science](https://stage.anaconda.com/report/2022-state-of-data-science/): 2022 State of Data Science This year, we conducted our State of Data Science survey to gather demographic information about... - [Podcast: Data Engineering as a Scientific Tool](https://10.2.107.56:8443/resources/podcast/data-engineering-as-a-scientific-tool): Show Notes In this episode, host Peter Wang is joined by Dr. Patrick Kavanagh, an astrophysicist and software developer at... - [Optimizing Python for Speed and Compatibility](https://10.2.107.56:8443/resources/podcast/optimizing-python-for-speed-and-compatibility): Show Notes In the penultimate episode of season one, host Peter Wang and Carl Meyer, Software Engineer at Instagram (owned... - [Climate Science, Scientific Computing, and Data Accessibility](https://10.2.107.56:8443/resources/podcast/climate-science-scientific-computing-and-data-accessibility): Show Notes This episode’s conversation between host Peter Wang and Ryan Abernathey, Associate Professor at Columbia University in the City... - [Shaping Best Practices for Monitoring ML Models](https://10.2.107.56:8443/resources/podcast/shaping-best-practices-for-monitoring-ml-models): Show Notes In this episode, host Peter Wang is joined by Elena Samuylova, CEO and Co-Founder of Evidently AI. Peter... - [Podcast: Snowflake and Advanced Analytics](https://10.2.107.56:8443/resources/podcast/unifying-and-accelerating-data-science-ml-and-advanced-analytics-workflows): Show Notes anaconda-snowflake-and-advanced-analytics-podcast-episode6-transcriptIn this episode, host Peter Wang speaks with Torsten Grabs, Director of Product Management at Snowflake, about how... - [Podcast: Modern Complexity and the Cybernetic Future](https://10.2.107.56:8443/resources/podcast/autopoiesis-in-systems-of-people-and-machines): Show Notes In “Autopoiesis in Systems of People and Machines,” Peter Wang welcomes Paco Nathan. Paco is a Managing Partner... - [Podcast: From Enthusiastic User to pandas Maintainer](https://10.2.107.56:8443/resources/podcast/from-enthusiastic-user-to-pandas-maintainer): Show Notes On this episode of Numerically Speaking: The Anaconda Podcast, host Peter Wang welcomes pandas maintainer Jeff Reback, Managing... - [Podcast: A Specialized Approach to Hardware](https://10.2.107.56:8443/resources/podcast/a-specialized-approach-to-hardware): Show Notes End users who are not schooled in hardware can often default to, “just give me something that works.... - [Podcast: Human in the Loop](https://10.2.107.56:8443/resources/podcast/human-in-the-loop): Show Notes Machine learning (ML) has reached an exciting phase of development, a phase that Vicki Boykis, Senior ML Engineer... - [Software, Venture Capital, and the Future of Work](https://10.2.107.56:8443/resources/podcast/software-venture-capital-and-the-future-of-work): Show Notes While today’s software may seem magical compared to that of previous generations, it still takes multiple software iterations... - [Introducing Numerically Speaking: The Anaconda Podcast](https://10.2.107.56:8443/resources/podcast/introducing-numerically-speaking-the-anaconda-podcast): Show Notes In this introductory episode of Numerically Speaking: The Anaconda Podcast, Anaconda CEO Peter Wang provides an overview of... - [State of Data Science 2021](https://10.2.107.56:8443/resources/whitepaper/state-of-data-science-2021): State of Data Science 2021 The 2021 State of Data Science report looks at how data science as a field... - [2020 State of Data Science](https://10.2.107.56:8443/resources/whitepaper/state-of-data-science-2020): 2020 State of Data Science The State of Data Science 2020 Moving from hype toward maturity The good news is,... --- ## Partners - [Accelerate AI Development and Deployment with Lenovo and Anaconda](https://10.2.107.56:8443/partners/directory/lenovo): Lenovo, the #1 PC company in the world, powered by Intel Xeon processors on Lenovo Workstations, offers an optimal solution to support Anaconda software for AI deployments and software development. - [Anaconda Collaborates with Microsoft to Enable Seamless Open-Source Innovation for Customers](https://10.2.107.56:8443/partners/azure): You can now confidently access Anaconda's curated library of open-source packages within Microsoft Cloud-hosted products and services like Azure Machine Learning. - [Anaconda Repository for IBM Cloud Pak for Data and for IBM watsonx.ai](https://10.2.107.56:8443/partners/ibm): With IBM Cloud Pak for Data and IBM watsonx.ai, you can automate AI lifecycles with comprehensive, curated open-source libraries and tools provided by Anaconda. - [Anaconda on Linux on IBM Z and LinuxONE](https://10.2.107.56:8443/partners/ibm-z): Bringing Anaconda to Linux on IBM Z and LinuxONE expands the availability of key open-source data science tools across platforms and improves practitioners' experience. - [Seamlessly Leverage Anaconda on OCI](https://10.2.107.56:8443/partners/oci): Anaconda and Oracle Cloud Infrastructure bring you secure open-source Python and R tools and packages by embeddingAnaconda’s repository across OCI AI and ML services. - [Leverage the Power of Graviton2 with Anaconda](https://10.2.107.56:8443/partners/aws): Anaconda for Linux on the aarch64 (arm64) platform optimized for AWS’s Graviton2 processors enables end-to-end data science in the cloud, from development to production. --- ## Events - [Meet Anaconda at Databricks World Tour Los Angeles 2025](https://stage.anaconda.com/event/meet-anaconda-at-databricks-world-tour-los-angeles-2025): Meet Anaconda in LA! Databricks World Tour Sept 23 2025 Stop by our booth and ask to see a demo!... - [Anaconda at Ai4 2025](https://stage.anaconda.com/event/anaconda-at-ai4-2025): Anaconda at Ai4 2025 Come see us at Booth #523 Ready to transform your AI strategy? Let’s discuss your AI... - [Anaconda at SciPy 2025](https://stage.anaconda.com/event/scipy-conference): Discover how Anaconda empowers enterprises, practitioners, and partners to simplify AI and data science on CUDA with GPU-accelerated workflows. - [Anaconda Executive AI Roundtable](https://10.2.107.56:8443/events/executive-ai-roundtable): RSVP for the Anaconda Executive AI Roundtable on July 31, 2025. Join our Chief AI Officer and Managing Director at Insight Partners in New York City to discuss AI business impact strategies. - [Anaconda at Microsoft Ignite](https://stage.anaconda.com/events/microsoft-ignite): Visit Anaconda’s Booth #P9 at Microsoft Ignite 2024 --- ## Leadership - [Test Leadership Bio](https://stage.anaconda.com/leadership/test-leadership-bio): Laura Sellers Co-President and Chief Product and Technology Officer Laura Sellers, Co-President and Chief Product and Technology Officer at Anaconda... - [Laura Sellers](https://10.2.107.56:8443/about-us/leadership/laura-sellers): Laura Sellers Co-President and Chief Product and Technology Officer Laura Sellers, Co-President and Chief Product and Technology Officer at Anaconda... - [Jane Kim](https://10.2.107.56:8443/about-us/leadership/jane-kim): Jane kim Co-President and Chief Commercial Officer Jane Kim, Co-President and Chief Commercial Officer at Anaconda, is a seasoned executive... - [Mark Mitchell​](https://10.2.107.56:8443/about-us/leadership/mark-mitchell): Mark Mitchell Senior Vice President, Strategy and Operations Mark Mitchell, Senior Vice President, Strategy & Operations at Anaconda, where he... - [Megan Niedermeyer](https://stage.anaconda.com/leadership/megan-niedermeyer): Megan Niedermeyer Chief Legal Officer Megan Niedermeyer, Chief Legal Officer at Anaconda, is a seasoned legal and operational leader for... - [Nitin Mittal](https://10.2.107.56:8443/about-us/leadership/nitin-mittal): Nitin Mittal Chief Financial Officer Nitin Mittal, Chief Financial Officer at Anaconda, has more than 20 years of finance experience... - [Peter Wang](https://10.2.107.56:8443/about-us/leadership/peter-wang): Peter Wang Chief AI and Innovation Officer and Co-founder Peter Wang is the Chief AI and Innovation Officer and Co-founder... - [Vanessa Macllwaine](https://stage.anaconda.com/leadership/vanessa-macllwaine): Vanessa Macllwaine Chief People Officer Vanessa MacIlwaine, Chief People Officer at Anaconda, where she leads the company’s global People strategy,... --- ## Legal Pages - [Master Subscription Agreement](https://stage.anaconda.com/legal/master-subscription-agreement): These terms govern your purchased subscription if noted in your Order Form or relevant purchase document. - [Terms of Service](https://stage.anaconda.com/legal/terms/terms-of-service): The Terms of Service for Anaconda’s different websites, Offerings, and separate products or services provided by Anaconda. - [Anaconda.org Terms of Service](https://10.2.107.56:8443/legal/terms/anaconda-org): This page lists the legal terms applicable to Anaconda.org and community-provided channels at Anaconda.org. - [Anaconda Toolbox in Microsoft Excel Terms](https://10.2.107.56:8443/legal/terms/toolbox): These terms govern Anaconda Toolbox in Microsoft Excel by Anaconda. - [Miniconda End User License Agreement](https://10.2.107.56:8443/legal/terms/miniconda): Miniconda End User License Agreement - [Acceptable Use Policy](https://10.2.107.56:8443/legal/terms/acceptable-use): This is the Acceptable Use Policy applicable to the Anaconda Platform. - [Anaconda DMCA Policy](https://10.2.107.56:8443/legal/terms/dmca): Anaconda Digital Millennium Copyright Act Policy (DMCA Policy) - [Anaconda Trademark & Brand Guidelines](https://10.2.107.56:8443/legal/terms/trademark): These Guidelines are designed to ensure proper legal use of the Anaconda Marks and to prevent confusion that can result from improper or illegal usage. - [Anaconda Events Code Of Conduct](https://10.2.107.56:8443/legal/terms/events): Events Code of Conduct for all who participate at Anaconda events - [Website Terms Of Use](https://10.2.107.56:8443/legal/terms/website-terms-of-use): These Terms of Use govern use of Anconda’s websites. - [Academic Policy](https://10.2.107.56:8443/legal/terms/academic): This Academic Policy (this “Policy”) outlines eligibility criteria, benefits, discounting eligibility, and how to sign up for Anaconda’s Academic Program. - [Embedded End Customer Terms](https://10.2.107.56:8443/legal/terms/embedded): This page describes the terms applicable to use of Anaconda’s Platform and Offerings via our Partner’s solutions. - [Non-Profit & Research Policy](https://stage.anaconda.com/legal/non-profit-research): This page describes Anaconda’s Non-Profit and Research Policy, including eligibility requirements. - [Edublocks Terms](https://10.2.107.56:8443/legal/terms/edublocks): This page details the legal terms applicable to Edublocks.org --- # # Detailed Content ## Pages > Reduce your organization's dependency on MLOps and empower your data scientists to innovate at unprecedented speeds. - Published: 2025-07-30 - Modified: 2025-08-07 - URL: https://stage.anaconda.com/capabilities/easy-deployment Easy Deployment Deploy your Machine Learning and AI projects with just one click. Get a Demo Simplify Model Deployment for Faster Innovation Bridge the gap between data science and MLOps. Streamline and simplify your deployment process to achieve a faster time to market, allowing you to edge out the competition. Improved Agility Accelerate innovation and agility in your data science projects. Empower your data scientists with rapid model iteration and experimentation, propelling their projects forward faster. Faster Time-to-Market Simplified model deployment enables you to achieve a faster time-to-market and rapidly deliver value to your stakeholders. Reduced Dependency Reduce your dependency on DevOps teams, empower your data scientists to drive progress autonomously. Cost Savings Optimize your model deployment to achieve significant cost savings with our streamlined approach that reduces the need for extensive DevOps involvement, extra infrastructure, and ongoing maintenance. Unlock efficiency and value for your organization effortlessly. Model Deployment Tools Get a robust set of tools and frameworks for deploying your ML models into production environments. Get out-of-the-box solutions for building interactive applications. Containerization Encapsulate your models into lightweight and portable containers. Ensure uniform deployment across diverse environments and platforms, enhancing the consistency and efficiency of your models. Resources The Definitive Guide to AI Platforms for Open-Source Data Science and ML Explore top open-source tools and use cases across industries. Act strategically to empower practitioners and teams to build, deploy, and maintain secure AI solutions. Learn More 2023 State of Data Science Report Read the latest report that gathered insights... --- - Published: 2025-07-17 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/thank-you/tech-talk-caio-unplugged Join us for an exclusive Tech Talk with Anaconda’s Chief AI and Innovation Officer and Co-founder, Peter Wang. In this interactive Q&A session, Peter shares expert insights on Python, AI, and the latest industry trends while answering key questions from participants. The discussion is moderated by Javvi Joyce Ferrer, Lifecycle Marketing Manager at Anaconda. Don’t miss this opportunity to learn directly from a leading AI expert! --- - Published: 2025-07-17 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/thank-you/automate-analysis-snowflake-anaconda-on-demand-video Learn the power of Snowflake SQL and Anaconda Python packages to simplify tasks that often require advanced tools or technical expertise. Get straightforward examples that help you implement these workflows with confidence. Understand how Snowflake can streamline analytics, unlocking new capabilities. Key Topics Include How to segment customers effectively (using requests) How to build a growth accounting framework (using pandas) How to perform accurate sales forecasting (using prophet) --- - Published: 2025-07-17 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/thank-you/women-illuminating-path-ai-success Join Forrester analyst Brandon Purcell and women tech leaders as they share proven strategies for building organization-wide AI capabilities. This discussion offers practical insights for accelerating AI adoption while ensuring responsible implementation. Key learnings include Building trust and drive AI literacy across teams through collaborative learning networks Bridging technical-business gaps with structured knowledge sharing and governance Creating scalable frameworks for AI education and skill development The talent shortage In honor of International Women’s Day, we are offering all webinar attendeesexclusive access to our learning platform free for 30 days! To claim this special offer, create an account and use the following code at checkout: IWDACCESS. Claim Your 30-Day Free Offer! --- - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/events Events --- > All Anaconda legal terms for your use of our different websites, Offerings, and separate products or services provided by Anaconda. - Published: 2025-07-03 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/legal/terms Terms and Policies This page provides a breakdown of relevant legal terms for your use of our different websites, Offerings, and separate products or services provided by Anaconda. For the full text of the Anaconda Platform Terms of Service, click here. All Offering Descriptions These terms are for specific features, applications, or add-ons (“Offerings”) that you may have access to as part of the Anaconda Platform. Your use of these Offerings are optional, and when you decide to use them, these terms will apply in addition to the Terms of Service. OfferingWhen these terms applyLinkAnaconda Toolbox for Excel When you choose to use Anaconda Toolbox for ExcelRead NowEmbedded End User License AgreementWhen you are accessing the Platform or Offerings through one of our authorized Partners’ solutionsRead NowAcademic End User License AgreementWhen you are using Anaconda on behalf of an Eligible Academic Institution. Read NowNon-Profit and Research End User License AgreementWhen you are using Anaconda on behalf of an Eligible Non-Profit and Research Organization. Read NowAnaconda. orgThese terms apply to you when you use Anaconda. org. Read Now Separate Terms These terms apply to standalone products or services from Anaconda, and do not fall under the Anaconda Terms of Service. Product or ServiceWhen these terms applyLinkEdublocksThese terms apply to you when you sign up for or use Edublocks (edublocks. org). Read NowMiniconda End User License Agreement (EULA)These terms apply to you when you use Anaconda. org. Read NowPythonAnywhereThese terms apply to you when you sign up for or use PythonAnywhere (pythonanywhere. com).... --- > Access our standard agreements, policies, and terms governing the use of our Platform and Offerings, as well as our Privacy Center and Trust Center. - Published: 2025-07-03 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal Anaconda Legal Welcome to Anaconda’s Legal Page. Here, you can access our standard agreements, policies, and terms governing the use of our Platform and Offerings, as well as our Privacy Center and Trust Center. For the full text of the Anaconda Platform Terms of Service, click here. Terms of Service Information about the legal agreements governing your use of Anaconda software products, including rights, restrictions, and obligations. Learn More Academic Institutions Details about eligibility requirements and special considerations for educational institutions using Anaconda for teaching and research. Learn More Non-Profit and Research Details about eligibility requirements for free use and special considerations for Research & Non-Profit Organizations using Anaconda. Learn More Privacy Center Information about our data collection practices, your privacy rights, and how we protect your information in compliance with global regulations. Learn More Trust Center Overview of our security frameworks, compliance certifications, and risk management procedures that safeguard your data and applications. Learn More All Terms & Policies A complete collection of Anaconda’s legal documents, usage guidelines, and compliance requirements for all platform users. Learn More Frequently Asked Questions Why did you change your Terms of Service in July 2025? We update our Terms of Service and policies from time to time to make sure they align with how we do business and to keep things clear for our customers. We also took to heart our community’s feedback and rewrote the terms with specific goals of clarity, accessibility, and transparency in mind. This update is focused on making... --- > See open roles across departments and countries.Read job descriptions and learn how to apply to available jobs at Anaconda, the operating system for AI. - Published: 2025-07-01 - Modified: 2025-07-02 - URL: https://10.2.107.56:8443/about-us/careers/jobs Job Openings --- > Anaconda's executive team includes best in class product and operational leaders, and is focused on scaling Anaconda to new heights. - Published: 2025-07-01 - Modified: 2025-07-01 - URL: https://10.2.107.56:8443/about-us/leadership The Leadership Team With decades of experience across all functions, the Anaconda leadership team collectively believes in building a long term viable business by putting the customer first. Executive Leadership Team --- > Build securely with enterprise governance, deploy faster with vetted models, and reduce risk with comprehensive security - Published: 2025-06-30 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/ai-platform/secure-governance Secure Governance Enterprise-Grade Protection for AI InitiativesDeploy AI with confidence using a secure framework that protects models, data, and workflows while enabling regulatory compliance Get a Demo Build AI Securely With Enterprise Controls Trusted Artifacts Access vetted packages and AI artifacts, enabling visibility to address vulnerabilities while accelerating development Vunerability Scanning Comprehensive vulnerability scanning packages, dependencies, and environments Automated Security Policies Define and enforce custom security policies, tailored to your compliance standards License Filtering Ensure adherence to open-source license compliance and filter out non-compliant licenses Regulatory Compliance Support Built-in security features support compliance with GDPR, HIPAA, and CCPA requirements Enterprise-Grade Secure Python Package Management Trusted Distribution More than 4,000 vetted Python packages, complete with dependency management and security controls Access Our Distribution AI Governance Establish clear policies and controls across your data science and AI projects Discover More Secure Package Management Protect your organization with verified, vulnerability-scanned packages and enforced security policies Dive Deeper Additional Resources The Total Economic Impact of Anaconda Study shows Anaconda AI Platform delivers significant cost savings and efficiency Learn More Anaconda Package Download Data Improved package statistics with more accurate download counts and better tracking of . conda artifacts Discover More Anaconda Recognized for Excellence in AI Innovation The Business Intelligence Group has recognized our platform in the Human-machine interaction – Product category Read Now Unlock the Full Potential of Open Source Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a... --- > Download and experiment locally with a curated repository of open-source large-language models with Anaconda AI Navigator. - Published: 2025-06-30 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/ai-platform/actionable-insights Actionable Insights Actionable Insights: Transform Data into Strategic ValueGain visibility into data science workflows with analytics that reveal usage patterns, identify bottlenecks, and quantify the impact of AI investments Discover Platform Insights Make Informed Decisions with Data-Driven Insights Granular Risk Assessment Get detailed insights into vulnerabilities and implement efficient and impactful mitigation strategies Security Compliance Monitor policy adherence and generate audit trails that simplify regulatory compliance reporting Package Intelligence Gain visibility into which packages are most utilized across your organization to guide standardization Transform Raw Data into Strategic Intelligence Error Tracking and Logging Centralized error tracking across workflows to identify and resolve issues faster with real-time monitoring Discover More Package Auditing Track package usage, identify vulnerabilities, and generate audit logs to ensure compliance Discover More Governance Use audit trails to support compliance with regulatory requirements, such as GDPR, HIPAA, and CCPA Learn More Additional Resources The Total Economic Impact of Anaconda Study shows Anaconda AI Platform delivers significant cost savings and efficiency Learn More Anaconda Package Download Data Improved package statistics with more accurate download counts and better tracking of . conda artifacts Discover More Anaconda Recognized for Excellence in AI Innovation The Business Intelligence Group has recognized our platform in the Human-machine interaction – Product category Read Now Unlock the Full Potential of Open Source Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your... --- > Download and experiment locally with a curated repository of open-source large-language models with Anaconda AI Navigator. - Published: 2025-06-27 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/ai-platform/trusted-distribution Trusted Distribution The Foundation for Secure AI DevelopmentBuild AI applications confidently using our secure Python package ecosystem, with thousands of validated packages, dependency management, and security controls Get a Demo Explore Our Trusted Distribution Accelerate AI Development with Trusted Open Source Validated Package Repository Build enterprise solutions with thousands of secure, Anaconda-curated packages Reduced Vulnerability Exposure Automated policy filters catch open-source vulnerabilities before application deployment Enterprise Policy Compliance Ensure packages meet your organization’s security requirements with customizable policy enforcement Air-Gapped Environment Support Maintain compliance in regulated environments with offline functionality and secure update mechanisms Enterprise-Grade Secure Python Package Management Secure Package Repository Access public packages and private repositories with security scanning and vulnerability detection Explore Packages Vulnerability Management Monitor open source vulnerabilities across components with detailed reporting and remediation guidance Discover More Deployment Flexibility Deploy your way across cloud, on-premises, containerized, air-gapped, and hybrid environments Explore Deployment Compliance Framework Ensure organizational compliance and regulatory requirements with configurable package policy enforcement Manage Governance Additional Resources The Total Economic Impact of Anaconda Study shows Anaconda AI Platform delivers significant cost savings and efficiency Learn More Anaconda Package Download Data Improved package statistics with more accurate download counts and better tracking of . conda artifacts Discover More Anaconda Recognized for Excellence in AI Innovation The Business Intelligence Group has recognized our platform in the Human-machine interaction – Product category Read Now Unlock the Full Potential of Open Source Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between... --- > Download and experiment locally with a curated repository of open-source large-language models with Anaconda AI Navigator. - Published: 2025-06-18 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/ai-platform Anaconda AI Platform Securely Accelerate AI InnovationStandardize, secure, and scale AI initiatives with a comprehensive platform that transforms open source complexity into enterprise-grade competitive advantage Get a Demo Build AI Faster and Securely With Open Source Build Once, Run Anywhere Avoid vendor lock-in with cloud-agnostic flexibility that supports your environment strategy Scale Governance Controls Balance security compliance with innovation velocity through enterprise-grade permissions Eliminate Fragmentation Streamline workflows with one unified platform that enables collaboration across teams and projects Enterprise-Grade Security Reduce security incidents by up to 60% with comprehensive vulnerability scanning and automated security policies Realize Measurable ROI Organizations implementing the Anaconda AI Platform achieve 119% ROI over three years ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access our Distribution Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights “Anaconda really is a bloodline, if you will, of the data science practice for my company. It enables development tools, statistical modeling, and package management, ensuring dependencies among packages with correct updates. ” PYTHON TECHNOLOGY LEADINDUSTRIAL Now Available on AWS Marketplace Deploy the Anaconda AI Platform into your existing AWS infrastructure Visit AWS Marketplace The Total Economic Impact of Anaconda Report Convert... --- - Published: 2025-04-23 - Modified: 2025-07-28 - URL: https://stage.anaconda.com/newsletter Be the First To Know Sign up for the Anaconda newsletter to be the first to hear about exciting news and content. --- - Published: 2025-04-18 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/partners/technology Technology Partners Our technology partners empower AI advancement through delivering the open-source Python and R tooling that simplifies and secures AI, ML, and data science workflows. Become a Partner Partnership Options Distributor Tap into the world’s largest Python community and enjoy deals and incentives. Approved Reseller Sell directly to Anaconda customers. Access training and support, and share in Anaconda’s success. Premier Reseller Sell to Anaconda customers, and install and provide tier 1 support for Package Security Manager. Ready to Get Started? Strategic Co-Marketing Together with your marketing team, we jointly promote our partnership value to the industry, customers, and prospects through webinars, events, and content. Enablement Access branding, collateral, subject matter experts, and other marketing support so you can run tailored demand-gen campaigns and go-to-market efforts. Demand Generation Anaconda promotes partnerships through PR and through campaigns that drive demand. Meet our Technology Partners What Our Partners Say "Anaconda offers many key benefits for the professional use of data science in enterprises such as security, flexibility and efficiency. With this partnership, we are looking forward to further advancing the development of secure Al and ML solutions in Europe. " Oliver Bracht Chief Data Scientist, Eoda "The mix of tools, technologies and processes aligned with a fantastic Anaconda sales and support team makes it easy to do business and provide customer insights in an agile, affordable, well-structured, fully governed manner. " Kielty Hughes CEO, ISx4 Become a Partner Interested in growing together? Learn More --- > Join Anaconda's team shaping the future of AI with open source. Enjoy exceptional benefits, career growth, and a remote-first culture in a diverse, award-winning company. Apply today! - Published: 2025-04-09 - Modified: 2025-08-13 - URL: https://stage.anaconda.com/about-us/ai-python-careers-anaconda Build the Future and Advance AI with Open Source at Anaconda Join the team that’s helping organizations simplify, safeguard, and accelerate their AI initiatives at scale. The award-winning Anaconda AI Platform provides a trusted ecosystem that powers innovation worldwide. See All Positions Life at Anaconda We’re not just a company; we empower organizations and builders to solve and innovate with data, inspiring breakthroughs that shape the future: Transform the World Contribute to building a platform used by millions around the world Drive Responsible Innovation Help organizations reduce risk and fully embrace AI. Create Measurable Impact Your work directly contributes to helping organizations drive value with AI. Diversity and Inclusion We believe that diverse perspectives drive innovation, equity creates opportunity, and inclusion helps everyone thrive. At Anaconda, we’re committed to building a culture where all kinds of people belong—because that’s how we create better products and serve our 50M users and community members. Career Growth: Scale Your Impact Career Mapping Discover clear pathways with transparent milestones and competencies necessary to reach career goals. Personalized Career Plans Develop skills that empower you to advance both our mission and your career. Safari Program Explore different departments and expand your expertise. What Our Team Has to Say Pioneers of Data Science “I am very proud and excited to be working at Anaconda; I feel we are shaping the next generation of artificial intelligence and machine learning. We at Anaconda are true pioneers with what we are building in the data science ecosystem — we... --- > Download Anaconda's open-source Distribution today. Discover the easiest way to perform Python/R data science and machine learning on a single machine. - Published: 2025-04-08 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/download-2 Distribution Register to get everything you need to get started on your workstation including Cloud Notebooks, Navigator, AI Assistant, Learning and more. Easily search and install thousands of data science, machine learning, and AI packages Manage packages and environments from a desktop application or work from the command line Deploy across hardware and software platforms Distribution installation on Windows, MacOS, or Linux Free Download Get access in 30 seconds. Completely free. * Get Started Returning Users *Subject to our Terms of Service. Use of Anaconda’s offerings at an organization of more than 200 employees/contractors requires a paid business license unless your organization is eligible for discounted or free use. See Pricing. Continue as Guest (Limited Access)Skip registration Distribution Free Download*Register to get everything you need to get started on your workstation including Cloud Notebooks, Navigator, AI Assistant, Learning and more. Easily search and install thousands of data science, machine learning, and AI packages Manage packages and environments from a desktop application or work from the command line Deploy across hardware and software platforms Distribution installation on Windows, MacOS, or Linux *Use of Anaconda’s offerings at an organization of more than 200 employees/contractors requires a paid business license. See Pricing Provide email to download Distribution Don't miss out! Get access to: Cloud Notebooks, Anaconda Assistant, easy application deployment, learning resources, and updates from Anaconda. *Email Address: Please enter your email address. *Opt In: Agree to receive communication from Anaconda regarding relevant content, products, and services. By continuing, I agree to Anaconda's... --- > Find the plan that's right for you by contacting the Anaconda sales team. Learn how to contact Support, and get contact info for Anaconda. - Published: 2025-04-07 - Modified: 2025-08-01 - URL: https://stage.anaconda.com/contact Contact Us Contact Sales Join the millions of organizations globally who use Anaconda to scale their AI initiatives. Fill out the form to talk to a sales representative today and learn what the Anaconda AI Platform can do for your business. Contact Anaconda Get Support If you are an Anaconda customer and need help, visit our resources. Support Center Anaconda. org Docs Community Partner with Us Tap into Anaconda’s extensive AI-focused customer network and deliver powerful AI-ready value to your customer base. Become a Partner Keep title simple and short Lorem ipsum dolor sit amet, consectetur adipiscing elit. 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10117 Berlin, Germany Keep title simple and short Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in. adipiscing Button --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2025-04-07 - Modified: 2025-07-31 - URL: https://stage.anaconda.com/ Anaconda Raises Over $150M in Series C Funding to Power AI for the Enterprise. Read More Advance AI with Clarity and Confidence Simplify, safeguard, and accelerate AI value with open source. Sign Up for Free Get a Demo ANACONDA AI PLATFORM The Only Unified AI Platform for Open Source Trusted distribution, simplified workflows, real-time insights, and governance controls you need to elevate practitioner productivity to save your organization time, money, and risk. Trusted Distribution The choice and customization to build your way with an expansive and curated selection of verified packages, artifacts, and models that just work. Learn More Secure Governance Effectively manage open source risk with embedded enterprise-grade governance, user management, and permissions. Learn More Actionable Insights Make better decisions with insights, predictive models & workflows, usage data, and recommendation engines. Learn More Scale with Confidence Calm the Chaos Anaconda’s unified approach frees you from silos, dependency blindspots, hyperscaler lock-in, debilitating vulnerabilities, lengthy development cycles, and failed pilots. Deliver Value with the Anaconda AI Platform Organizations achieve 119% ROI by centralizing their approach to sourcing, securing, building, and deploying AI with the Anaconda AI Platform. Millions Rely on Anaconda to Advance Their AI Initiatives 0 M Users Globally 0 M Developers andcontributors 0 M+ Global Organizations 0 % Fortune 500 companies The Total Economic Impact of Anaconda Report Convert open source complexity into enterprise advantage with $1. 18M in validated benefits over just three years. 119% ROI with an 8-month payback period $840,000 in operational efficiency improvements 60% reduction... --- > Learn how Anaconda enables enterprise open-source AI innovation with a platform designed to support compliance, security, and governance requirements. - Published: 2025-04-07 - Modified: 2025-05-08 - URL: https://10.2.107.56:8443/talk-to-an-expert Talk to an Expert Book time with one of our experts today to see how Anaconda enables enterprise open-source innovation with a platform designed to support compliance, security, and governance requirements. Achieve faster time-to-value by seamlessly sharing work across teams and deploying applications with a single click. Centralize workflows. Gain visibility and control over your open-source pipeline. Connect With Us --- > The hub for data science and AI collaboration. Source, build, and deploy with ease, using leading-edge tools that take your work from idea to integration. - Published: 2025-04-07 - Modified: 2025-04-07 - URL: https://10.2.107.56:8443/products The Hub for Data Science and AI Collaboration Source, build, and deploy with ease, using leading-edge tools that take your work from idea to integration. Start for free today. Create a Free Account Discover the Right Plan for You See Pricing Free Starter Brusiness Enterprise Start Today for Free Streamline application development and deployment with Cloud Suite. Create a Cloud Account Capabilities for the Data Science Lifecycle Source Start with clean, controlled data Data management AI governance Sample projects Secure package management Build Develop efficiently and use infrastructure that supports scaling AI Assistant Air-gapped environment Collaboration capabilities Error tracking On-demand infrastructure Resource management Data visualization Version control Deploy Get your work into the world and showcase its success Deploy APIs Streamlined application deployment Dashboard creation Tools and Services to Power Your Productivity Cloud Suite Code in your browser with Notebooks – no installation needed. Develop more efficiently with the AI-powered Assistant. Share applications easily with Panel app deployment. Create a Free Account Distribution Easily search and install thousands of Python and R packages for data science and AI projects. Manage those packages and environments from a desktop application. Download for Free Notebooks Start coding immediately and share dashboards and projects with stakeholders from the browser. No installation or configuration necessary. Learn More Package Security Manager Proactively manage risks and ensure compliance in your data science, machine learning, and AI projects. Use the OSS your team loves with comprehensive security. Learn More Navigator Work with packages and environments without needing to... --- > Solve data science and machine learning challenges alongside Anaconda's consultants, developers, and engineers. See what the professional services team can do for you. - Published: 2025-04-04 - Modified: 2025-08-14 - URL: https://stage.anaconda.com/professional-services Professional Services A force multiplier for your AI journey. Let our experts help you solve your toughest AI, data science, and machine learning challenges for faster development, fewer hiccups, and immediate impact. Read Datasheet Tell Us About Your Project Working with Professional Services Our experts will work with your team to optimize performance and create a lasting AI foundation. A Force Multiplier Reach your destination faster and avoid technical debt by avoiding common obstacles. Unmatched Expertise Our experienced team of scientists, engineers, and software developers specialize in open-source Python for machine learning, data analysis, and more. Built for Your Needs Whatever your use case, we can help you succeed, whether that’s optimizing code, building AI-powered workflows, or anything in between. Services Available Our professional services team will help your in-house experts achieve your business goals using Python. Tier 2 Support Get expert help with debugging, packaging, and optimizing conda for your organization. Kickstart Kickstarts help you migrate, adopt new tools, and build open-source solutions fast that go beyond Tier 2 Support. Customer Consulting Need more than Kickstarts? Let’s discuss custom projects and deep integrations to meet your goals. Kickstart Services Expert-led services to fast-track your AI and data science initiatives. Quickly roll out a new data initiative or fast-track an existing one. We offer pre-packaged short engagements (typically 100 hours) at a fixed price. The goal is to get your team using the Anaconda AI Platform for immediate impact. Tell Us About Your Project Environment Management Standardize and distribute Python/R... --- > Anaconda has supported open-source innovation and project maintenance in the form of employee time, direct donations, event sponsorships, and more. Learn More - Published: 2025-04-03 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/our-open-source-commitment Our Open-Source Commitment Nurturing the health and vibrancy of the OSS community for tomorrow’s innovation The Dividend Program In October 2020, we formalized a commitment to give a portion of our revenue directly back to the open-source community through the Anaconda Dividend Program. Since then: We launched the program in partnership with NumFOCUS. Anaconda CEO and co-founder Peter Wang continues to serve on the NumFOCUS Advisory Council. We have consistently exceeded our target donation amounts. Open-Source Development and Maintenance Our teams develop, contribute to, and maintain a variety of popular open-source projects including Dask, pandas, Numba, HoloViz, conda, BeeWare, PyScript, and more! We keep abreast of up-and-coming projects and are always open to working on new ones. Empowering Individuals and Small Businesses We offer free Anaconda Distribution access to individuals and organizations with fewer than 200 employees. It’s important to us to supply enterprise-grade data science tools for everyone, even those who may not have the resources to purchase them. OSS Resources Learn more about our ongoing commitment to open source 2021-2022: Anaconda’s Contributions to the Open-Source Community Read Now Propelling Python into the Next Decade: Anaconda’s OSS Vision Read Now 2021: The Anaconda Annual Dividend Program Report Read Now Learn More about Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science... --- > We are proud to distribute and contribute to a variety of open-source projects. Technologies for Data Science - Published: 2025-04-03 - Modified: 2025-07-24 - URL: https://10.2.107.56:8443/open-source Open Source Tools and libraries for data science, machine learning, and AI Support OSS You can help support ongoing innovation on projects in the open-source community. Your donation goes directly to NumFOCUS and supports their work. Support the Cause Get Started Many of the most commonly used open-source data science and machine learning packages are automatically installed when you download Anaconda Distribution and thousands of others. Download Distribution Table of Contents The Fundamentals Jupyter Jupyter is an open-source project created to support interactive data science and scientific computing across programming languages. Jupyter offers a web-based environment for working with notebooks containing code, data, and text. Jupyter notebooks are the standard workspace for most Python data scientists. Learn More Pandas A library for tabular data structures, data analysis, and data modeling tools, including built-in plotting using Matplotlib. pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis with Python. Learn More SciPy The SciPy library consists of a specific set of fundamental scientific and numerical tools for Python that data scientists use to build their own tools and programs. It provides many user-friendly and efficient numerical routines, such as routines for numerical integration, interpolation, optimization, linear algebra, and statistics. Learn More NumPy A core package for scientific computing with Python. NumPy enables array formation and basic operations with arrays. NumPy is used for indexing and sorting but can also be used for linear algebra and other operations. Many other data-science libraries for Python are built on... --- > IT Admins, learn how to stop vulnerabilities, not workflows. - Published: 2025-04-02 - Modified: 2025-07-09 - URL: https://10.2.107.56:8443/it-admins IT Admin Solutions Stop vulnerabilities, not workflows Request a Demo Protection against Risk Guard your python open-source supply chain against risks 0 Days 277 days is the amount of time it takes security teams to identify and contain a breach. 0 % data breaches 82% of data breaches are caused by human error, the most common threat vector. $ 0 million $9. 4 million is the average cost of a data breach in the United States Security Teams Assume Multi-Faceted Risks Visibility and Governance Python users can access software from untrusted sources, without security team reviews or control. Supply Chain Security Malicious packages lurk in the software supply chain and mitigating them is mission-critical. Data Protection Every laptop represents a potential attack surface for a costly data breach. Dependency Management Careful integration of open-source software with other components and systems is critical. Support and Maintenance Open-source software requires continuous updates to ensure secure environments. Full Visibility and Security for Python Packages Control User Access Leverage user management that allows you to govern access to data, packages, and models. Isolated Environments Curate required packages and dependencies that are compliant with regulatory frameworks. Audit Trails Track and manage the use of software packages to comply with licensing requirements. Centralized Resource Management One platform to manage open-source resources and guard against cost overruns. Role-Based Access Controls Authorize users who can install and run software packages and access critical data and software. Trusted by 90% of Fortune 500 Companies Learn More Single-Tenant SaaS Offerings... --- - Published: 2025-03-27 - Modified: 2025-08-20 - URL: https://stage.anaconda.com/resources/infographics Infographics --- > Anaconda's Package Security Manager presents a holistic and forward-thinking approach to managing package security within data science and machine learning environments. Learn More - Published: 2025-03-27 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/products/package-security-manager Package Security Manager Proactively manage risks and ensure compliance in your data science, machine learning, and AI projects with a comprehensive security solution. See Pricing Security in an Ever-Changing Landscape Tackle the complexities of securing software packages, managing vulnerabilities, creating security policies, and meeting compliance standards using Package Security Manager. Comprehensive Vulnerability Scanning Understand vulnerabilities for packages and their dependencies. Vulnerability Notifications Get updates on vulnerabilities that affect the packages you are using. Custom Security Policies Define and enforce custom security policies, tailored to your compliance standards. Granular Risk Assessment Get detailed insights into vulnerabilities and implement efficient and impactful mitigation strategies. Centralized Security Management Manage and track your organization from a central location. Streamline security operations and compliance requirements. License Filtering Ensure adherence to open-source license compliance standards and filter out licenses that do not align with your organization’s requirements. Centralized, Automated Package Security Management with Notifications Read our latest documentation. Cloud or On-premise Solutions Manage your organization’s package security in the cloud, or on-premise, including air-gapped networks. Cloud Docs On-prem Docs Access Control Control who has access to your Package Manager and the channels it contains. Cloud Docs On-prem Docs CVE Curation and Notifications Keep up to date with fluctuating vulnerability scores that affect packages in your applications. Cloud Docs On-prem Docs Package Signatures By utilizing cryptographic signatures, we ensure each package’s integrity and authenticity, protecting your projects from compromised or tampered software. Cloud Docs On-prem Docs Software Bill of Materials Generate detailed SBOMs to easily identify dependencies,... --- > Anaconda provides on-premises deployment options for LLMs, enabling organizations to securely host and utilize these powerful AI models within their own infrastructure - Published: 2025-03-27 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/products/on-premises-llm On-Prem LLM Use open-source large-language models to create custom generative AI applications tailored for your use cases, and host them on-premises. Get a Demo Create LLMs Tailored for Your Use Cases Developing an LLM is complex—and cloud-hosted proprietary models are expensive, not specific to a use case, and do not secure your organization’s data. Access Open-Source LLMs Test a variety of customizable large-language models with various parameter counts, sizes, and accuracy levels. Ensure Security Keep data secure with on-premises deployment for unparalleled flexibility and control over your generative AI projects. Learn from the Experts Anaconda’s Professional Services team will assist with implementation to ensure outcomes meet your requirements. Resources Generative AI with IBM and Anaconda Discover the power of Anaconda and IBM WatsonX for LLMs. Learn More Anaconda Professional Services Get help building and implementing generative AI from Anaconda Experts. Learn More Data Preparation for Large Language Models Learn how to transform and clean data for LLMs. Take the Course Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo FAQ What is an on-premise LLM solution? An on-premise LLM solution is a large language model deployed within your organization’s own infrastructure. A cloud-based LLM, on the other hand, operates on external infrastructure managed by a cloud service provider. What are the benefits of an on-premise... --- > Notebooks allow anyone, anywhere to spin up powerful data science projects directly from your browser, with all the packages and computing power you need. - Published: 2025-03-27 - Modified: 2025-07-31 - URL: https://stage.anaconda.com/products/notebooks Toolbox for Notebooks Skip setup and installation and get straight to learning and writing code in Notebooks Create Cloud Account. Expand Your Knowledge with Notebook in Cloud Start coding immediately Notebooks allow anyone, anywhere to begin their data science journey. Spin up awesome data science projects directly from your browser with all the packages and computing power you need. Seamlessly share your work Share your work effortlessly with a click-through URL or step it up with a dynamic Panel App deployed to the web, offering instant access to your creations from anywhere. No API keys, no hassle Use the power of AI Assistant and your Cloud account to generate API keys and deal with access-based permissions. Ready to Get Started? Code from Anywhere Log in and pull up conda configurations wherever you are online. Whether you want to upload a local environment or directly manage packages in the notebook — we’ve got you covered! Seamless Integration Easily transform your models into interactive applications using Panel’s straightforward integration with the Anaconda ecosystem and popular data science libraries. Enhance Your Skills Leverage our Learning platform and AI assistant to expand your Python and data science knowledge, irrespective of whether you’re a beginner or seasoned professional. Streamline Access to Your Data Data Connectors allow you to save your data file (. csv) on Anaconda Cloud for easy access from Excel or from a notebook. AI Assistant Anaconda Assistant for Python can analyze your tables and recommend different ways of working with your data.... --- > Work with the packages you want, install in any environment, and run and update them without needing to type conda commands in a terminal window. Learn More - Published: 2025-03-27 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/products/navigator Anaconda Navigator Launch your data science applications from your desktop with AI navigator Download Now Terminal Window Not Required Find the packages you want, install them in an environment, run the packages, and update them – all inside Navigator. Launch applications and manage conda packages, environments, and channels without using command line interface (CLI) commands. Search for packages on Anaconda. org or in a local Anaconda Repository. Available for Windows, macOS, and Linux. Anaconda Assistant for Python can analyze your tables and recommend different ways of working with your data. Data Connectors allow you to save your data file (. csv) on Anaconda Cloud for easy access from Excel or from a notebook. Next Generation of Anaconda Navigator As part of our commitment to making data science accessible, we are continuing to develop the next generation of Anaconda Navigator. Download Now Anaconda Navigator will soon include: Implementation Resources Get access to user authentication, shared environments, storage, and access to computing resources are all set up for you during implementation. Customize an email campaign Ensure that your team gets all the benefits of centralized notebooks without the IT burden of maintaining JupyterHub. Automatic Configuration Access automatic configuration to use the secure and trusted packages from your Anaconda Server instance. Additional Resources Anaconda AI Assistant Comes to the Desktop Learn More Get started using Navigator Read the Docs Deployments Complete hands-on training courses on the cloud or on your machine View Course Catalog Get Navigator To access Navigation, download Distribution for free.... --- > Decrease risk and increase productivity with Anaconda’s repository of curated, rigorously tested, and verified data and models. - Published: 2025-03-27 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/products/models Source Secure, Curated Data and Models Explore Anaconda’s repository of curated, rigorously tested, and verified data and AI models. Get a Demo A Single, Trusted Source Anaconda curated and tested data and models for AI projects. Validation Anaconda validates, stores, and distributes each dataset and model. Centralization Get access to all the models and packages you need in one place. Security Confirm models are safe by looking for the Anaconda signature verification. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights The Total Economic Impact of Anaconda Report Convert open source complexity into enterprise advantage with $1. 18M in validated benefits over just three years. 119% ROI with an 8-month payback period $840,000 in operational efficiency improvements 60% reduction in security vulnerabilities ($157,000 value) $179,000 in technology cost savings Read Forrester TEI Report Additional Resources Anaconda AI Platform Learn More Shaping Best Practices for Monitoring ML Models Learn More Making Data, Models, and Analytics Awesome Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and... --- > Moving from idea to solution involves a complex network of dependencies and environments and can delay business insights. Anaconda simplifies that complexity. - Published: 2025-03-27 - Modified: 2025-07-09 - URL: https://10.2.107.56:8443/practitioners With One Platform, What Will You Build? Start Coding Now Data Analysis and Machine Learning Projects Are Complex Difficult to Start Moving from idea to solution involves a complex network of package dependencies and environments. It Works On My Machine When Python workflows run on one machine with unique environments, collaboration and reproducibility suffer. Deployment is Complex Moving models into production requires significant work that can delay valuable insights and prevent the achievement of business value. Customize an email campaign Practitioners must continuously learn, train, and refine their skill sets with high-quality training opportunities. Customize an email campaign Working with open-source software requires access to reliable support and documentation for community-maintained tools. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Unleash Innovation, Backed by the Python Experts Community Resources Tap into Anaconda’s vast ecosystem of open-source software packages for data science, machine learning, and beyond. Expert Package Manager Get one-click access to open-source tools, with environments, dependencies and package compatibilities handled for you. Powerful and Versatile Avoid recoding by integrating the best open-source tools from across coding languages into your workflows.... --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2025-03-26 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/installation-success Welcome to Anaconda! Create your free Anaconda Cloud account today to get access to training materials, how-to videos, and expert insights, all free for a limited time to Nucleus members. Register for Free Anaconda Distribution Tutorial Anaconda Distribution is the industry standard for data scientists developing, testing, and training on a single machine. Learn how to get started with Anaconda Distribution, work with conda, and write your first Python program in these short tutorials. Get Started http://stage. anaconda. com/wp-content/uploads/2025/03/Installer-Success. mp4 Anaconda Documentation Visit our extensive library of documentation for support with installation, packages, integrations, environment configurations, navigator, and so much more. Visit Anaconda Docs For further support, visit our community forum. Learn More --- > Anaconda is ISO 27001 certified and prioritizes security through data encryption, access control, and an organizational commitment to secure practices. - Published: 2025-03-26 - Modified: 2025-07-12 - URL: https://10.2.107.56:8443/security-compliance Security and Compliance Your Security is Our Priority You need secure data and trustworthy tools to keep up in today’s business landscape. Anaconda’s multifaceted security and compliance practices enable that security. Data Encryption and Processing Anaconda ensures data storage encryption at the disk level and within the database. Only mission-critical data is processed, and all application, database, webhook, and API traffic is encrypted via TLS/HTTPS. Security Measures A third-party firm conducts annual security and penetration testing. Anaconda routinely addresses discovered vulnerabilities. Other measures include full-disk encryption, VPNs, password managers, and 2FA. Access Control Only Anaconda-authorized team members have access to account data, and all employees undergo background checks. Organizational Commitment Initiatives around customer data security, vulnerabilities, and supply chain security are led by the Anaconda Engineering leadership team and Security Guild. All Anaconda employees receive security awareness training. ISO 27001 Certification From the International Organization for StandardizationISO 27001 certification promotes a holistic approach to information security: vetting people, policies, and technology. All Anaconda suppliers are ISO or SOC2 certified. Download Security Certificate Compliance View All Compliance Documentation Privacy Center Through encryption, the data you request access to is kept a secret between you and Anaconda. Learn More Privacy Policy We respect your privacy, and this Privacy Policy describes our practices with respect to Personal Information we collect from or about you when you use our Services. Learn More --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2025-03-26 - Modified: 2025-04-18 - URL: https://10.2.107.56:8443/tos-access Access Denied We’re sorry! Your access to the Anaconda package repository and installer downloads has been blocked in accordance with our Terms of Service or due to a security issue. We support innovation in the open-source community by asking commercial users to purchase a license. But we can get you back up and running quickly. Get Support Contact Us --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2025-03-26 - Modified: 2025-04-03 - URL: https://10.2.107.56:8443/tos-error Access Denied! We’re sorry! Your access to the Anaconda package repository and installer downloads has been blocked in accordance with our terms of service or due to a security issue. But we can get you back up and running quickly! Contact Us Purchase License --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2025-03-26 - Modified: 2025-04-03 - URL: https://10.2.107.56:8443/tos-mirroring Access Denied We’re sorry! Your access to the Anaconda package repository and installer downloads has been blocked in accordance with our Terms of Service or due to a security issue. If you think your request was blocked in error, please reach out to Customer support, or if you need to mirror the Anaconda Offerings, visit our pricing plans or contact sales. Contact Us Pricing and Plans --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2025-03-26 - Modified: 2025-07-31 - URL: https://stage.anaconda.com/download-success Thank You for Downloading! Didn’t download? Go here to download your version. Create an AccountGet added benefits of Notebooks, Panel App Deployment, AI Assistant, Data Catalogs, and Anaconda Learning. Create Free Account Quick Start All the tools you need to bring your projects to life Distribution Course Take an introductory course on Distribution, conda, and how to create your first Python program. View Course Notebooks – Code Online Prefer to code in your browser? Start coding immediately with Notebooks! No installation or configuration necessary. Launch Notebooks Cloud Environment Backup Connect Anaconda Navigator to our community portal via AI Platform to securely store your local environments in the cloud. Learn More Learn Python Basics in 3 hours Learn how to read and write Python code. Solve real-world problems with loops and functions, and create your own objects and functions. Start Course Discover More Learn Data Science Skills Learn the most essential skills in Python, data analysis, visualization, machine learning, and more from expert instructors with Starter Plan. See Pricing Anaconda Community Join the Anaconda Community to get expert advice and knowledge directly from Anaconda Users. Get the latest info and insights. Learn more Conda Open-source package and environment management system that runs on Windows, macOS, and Linux. Install, run, and update packages and their dependencies. Learn more --- > The go-to-market kit includes assets, collateral, and guidelines for adding Anaconda to your partner directory, enable your sales teams, and engage your audience. - Published: 2025-03-26 - Modified: 2025-06-16 - URL: https://10.2.107.56:8443/partner-brand-kit Partner Go-To-Market Resources Welcome Partners! This Go-To-Market (GTM) kit includes assets, collateral and guidelines you will need to add Anaconda to your partner directory, enable your sales teams, and engage your audience. Once you’ve accessed the kit, make sure to download and review the Brand Guidelines first. The guide contains Anaconda’s logo usage guidelines, corporate colors, and guidelines for creating logo lockups. To share your logo and other brand assets with Anaconda by emailing Partner Marketing. Please include a logo and a “bug” icon in 1:1 aspect ratio, both in . png format with transparent backgrounds. --- > Anaconda Toolbox for Excel enhances Microsoft Excel with a suite of tools built to make coding in Python even easier. - Published: 2025-03-26 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/products/anaconda-toolbox Anaconda Toolbox for Microsoft Excel Make working in Python even easier Read Docs Get Access Power Up Your Data Analysis Quickly generate code and visualizations, learning Python as you go, all powered by Anaconda Sync Your Data Anywhere Start in one workbook, pick up in another—your data follows you when all your work is saved to Anaconda Cloud. Enabled In Your Workstream Simple-to-use tools bring the power of Python directly into your spreadsheet. Robust Visualizations Transform tables into powerful visuals and uplevel your data in a guided and intuitive interface. Low-Code Solutions Python knowledge not required. Use the AI-powered assistant for Python-centric tasks in Excel. Take Features to the Next Level A suite of tools to maximize all that Python in Excel has to offer Get Access Visualization Builder Create advanced and powerful data visualizations with the click of a button. Datasets Datasets allow you to save your data file (. csv) on Anaconda Cloud for easy access from Excel or from a notebook. AI Assistant Anaconda Assistant for Python in Excel can analyze your Python code and recommend different ways of working with your data. Code Snippets Carry your code between Excel workbooks and Anaconda Notebooks using code snippets. Work Locally with Anaconda Code Write Python code and run it locally, directly within Excel. Get flexibility and control over the Python environment, and add and remove packages as needed, all while keeping code and data securely within your workbook. Read Docs Related Resources for Python in Excel Anaconda Toolbox... --- > Flexible pricing plans that scale with your AI needs. Free, Starter, Business, and Enterprise tiers offer tailored solutions for your data science and ML projects. - Published: 2025-03-25 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/pricing Plans and Pricing Flexible pricing plans that scale with your AI needsUse of Anaconda by those at organizations with more than 200 employees or contractors (including Affiliates) requires a paid Business license. If you are at an academic, non-profit or research institution you may be exempt from the paid license requirement. For more information, see our full Terms of Service, or read our Frequently Asked Questions. FREE $0 For the individual practitioner who needs a basic workspace Start for Free Basic workspace for the student or practitioner 5G for shareable, cloud-hosted notebooks Thousands of curated packages Anaconda Assistant STARTER $15 month/user For the individual practitioner or small team that requires more features Get Started Free for academic accountsUp to 15 seats (16+ Contact sales)Free with expanded seat count under Anaconda for Education and Anaconda’s Academic Policy. EVERYTHING IN FREE, PLUS: Better workspace for the academic or practitioner 10GB for shareable, cloud-hosted notebooks On-demand training courses BUSINESS $50 month/user For organizations that need robust features and capabilities Start for Free Annual subscription plan
up to 15 seats (16+ Contact sales)EVERYTHING IN STARTER, PLUS: Open-source software supply chain security tools Curated vulnerability data Enterprise Single Sign-On with Directory Sync Advanced collaboration, and governance tools CUSTOM $0 For organizations that need custom capabilities beyond our existing plans Start for Free Tailored pricing to help you scale, innovate, and deliver value faster. Compare Plans Organizations Free $0 per user/month Get Started for up to 1 seat Recommended Starter $15 per user/month Get Started Free for... --- - Published: 2025-03-25 - Modified: 2025-08-07 - URL: https://stage.anaconda.com/download-2/download-success Download Now Download Anaconda Distribution or Miniconda by choosing the proper installer for your machine. Learn the difference from our Documentation. Distribution Installers Download Download for Mac Download for Apple Silicon Download for Intel For installation assistance, refer to troubleshooting. Windows Python 3. 13 64-Bit Graphical Installer (914M) Mac Python 3. 13 64-Bit (Apple silicon) Graphical Installer (754M) 64-Bit (Apple silicon) Command Line Installer (757M) 64-Bit (Intel chip) Graphical Installer (776M) 64-Bit (Intel chip) Command Line Installer (778M) Linux Python 3. 13 64-Bit (x86) Installer (1. 04GB) 64-Bit (AWS Graviton2 / ARM64) Installer (886M) Python 3. 13 64-Bit Graphical Installer (914M) Python 3. 13 64-Bit (Apple silicon) Graphical Installer (754M) 64-Bit (Apple silicon) Command Line Installer (757M) 64-Bit (Intel chip) Graphical Installer (776M) 64-Bit (Intel chip) Command Line Installer (778M) Python 3. 13 64-Bit (x86) Installer (1. 04GB) 64-Bit (AWS Graviton2 / ARM64) Installer (886M) Miniconda Installers Download Download for Mac Download for Apple Silicon Download for Intel For installation assistance, refer to troubleshooting. Windows Python 3. 13 64-Bit Graphical Installer Mac Python 3. 13 64-Bit (Apple silicon) Graphical Installer 64-Bit (Apple silicon) Command Line Installer 64-Bit (Intel chip) Graphical Installer 64-Bit (Intel chip) Command Line Installer Linux Python 3. 13 64-Bit (x86) Installer 64-Bit (AWS Graviton2 / ARM64) Installer Python 3. 13 64-Bit Graphical Installer Python 3. 13 64-Bit (Apple silicon) Graphical Installer 64-Bit (Apple silicon) Command Line Installer 64-Bit (Intel chip) Graphical Installer 64-Bit (Intel chip) Command Line Installer Python 3. 13 64-Bit (x86) Installer 64-Bit (AWS Graviton2... --- > Download and execute curated open-source large language models(LLMs) secure on your desktop with Anaconda AI Navigator. - Published: 2025-03-25 - Modified: 2025-08-08 - URL: https://stage.anaconda.com/products/ai-navigator Anaconda AI Navigator Download and experiment with curated open-source AI models locally. Download for Free Simplify and Safeguard Your Use of LLMs AI Navigator gives you easy access to a variety of large language models with various parameter counts, sizes, and accuracy levels so you can find the right model for your specific device — in a secure desktop environment. Secure Run large-language models locally to maintain privacy and data security. Private Interact with local LLMs without sending data through cloud services or infrastructure providers. Low Risk Work with different leading LLMs to discover the best models for your use case. A Proving Ground for GenAI Access LLMs that have been curated, hosted, and verified by Anaconda. AI Navigator lets you experiment with LLMs in the secure environment of your desktop, then interact with them through an API server or a chatbot — all working locally, so your data stays secure. Download for Free Curated Models Choose from Anaconda’s trusted library of over 200 leading LLMs—55 models, each with four different quantization levels—so you can gauge model efficiency and accuracy. API Inference Server Test models without the need for external cloud services. Enhance security by replacing calls to proprietary LLM providers with calls to the local server. Built-in AI Assistant Accelerate common tasks like summarization of long-form text and strategy generation, all with the security and privacy of local chat. Local Models Work with LLMs locally and securely, keep control of proprietary information, and eliminate the need for an internet... --- > Anaconda Business Plan allows your data science, ML, and AI teams to us - Published: 2025-03-25 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/pricing/business Anaconda Business Plan See Pricing PREFERRED BY USERS 47M+ Global Users Number of active users in one of the largest open-source Python communities BUILT FOR THE ENTERPRISE 250K+ Organizations Rely on Anaconda to drive innovation, including 90% of the Fortune 500 POWERED BY THE COMMUNITY $40M+ to OSS Investments in open-source innovation and maintenance The Tools You Love. The Security You Need. Leading-edge tools to power up your business Data Scientists and Analysts Quickly develop and deploy models with the confidence that packages meet security requirements. IT Administrators Empower productivity and ensure security with role-based access controls. Security and InfoSec Leaders Proactively define enterprise-grade policies with license and package filtering. Faster, Safer AI with Open Source Work Faster, Save Money Jumpstart your data science and AI projects with pre-built environments and thousands of Python and R packages. Pair Program with AI The AI Assistant speeds up coding, generating plots, describing DataFrames, debugging errors, and more. Manage Risks and Compliance Centralize security management and leverage custom policy filters and vulnerability notifications. APIs and Interactive Dashboards Inference APIs let your AI models communicate with the world. Interactive dashboards surface key insights, faster. Advanced Notebooks and Publishing Get cloud Notebooks with 20GB of storage and 20K seconds daily CPU/compute. Publish apps simply, with Panel. BY THE NUMBERS The AI Platform Made for Results Leading-edge tools to power up your business with real savings 5 developers On average, Anaconda saves companies the time and cost of 5 developers. 
 7X more accuracy Anaconda provides... --- - Published: 2025-03-21 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/topics Topics --- > Unlock data's business value faster, with thousands of multi-platform, multi-language open-source packages curated and secured by Python experts, with the Enterprise plan. - Published: 2025-03-21 - Modified: 2025-06-27 - URL: https://10.2.107.56:8443/enterprise Develop and Deploy Secure Python Solutions Unlock data’s business value faster, with thousands of multi-platform, multi-language open-source packages curated and secured by Python experts Request a Demo PREFERRED BY USERS 45MM+ Users 45MM active users in one of the largest open-source Python communities BUILT FOR THE ENTERPRISE 250K+ Organizations 250K+ organizations rely on Anaconda to drive innovation, including 93% of the Fortune 500 POWERED BY THE COMMUNITY $40MM+ to OSS $40MM+ contributed to open-source innovation and maintenance One Platform Powers AI and Data Science Anaconda’s platform empowers individuals and organizations to develop and deploy secure Python solutions, faster. Build Develop myriad Python solutions, from simple dashboards to powerful deep learning applications. Learn More Deploy Centralize collaboration, ease reproducibility, and enjoy one-click deployment, so you can spend more time tackling your next project Learn More Secure Empower scalable, secure workflows while ensuring governance, security, and compliance. Learn More Preferred by Users, Built for the Enterprise Python Users Tackle any challenge: develop, collaborate, and deploy with trusted open-source tools Learn More IT Managers Stop risks, not workflows: Empower Python users with governance, security, compliance. Learn More Accelerating Open Source Innovation in the Enterprise Over 250K organizations | Over 250 billion packages downloaded Finance Leaders in finance, banking, and insurance are applying Python and moving to open-source software to establish an edge in competitive markets. Learn More Healthcare Anaconda provides on-stop access to the best open-source tools across the many disparate use cases common to healthcare. Learn More Manufacturing Enterprise practitioners in manufacturing... --- - Published: 2025-03-20 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/resources/podcast Podcasts --- - Published: 2025-03-20 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/resources/case-study Case Studies --- - Published: 2025-03-20 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/guides Guides --- - Published: 2025-03-20 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/resources/report Reports --- - Published: 2025-03-20 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/resources/video Videos --- - Published: 2025-03-20 - Modified: 2025-03-25 - URL: https://10.2.107.56:8443/resources/whitepaper White Paper --- > Avoid Duplication of Efforts and Misalignment of Priorities. Effective team collaboration is essential for leveraging expertise, sharing knowledge, and maintaining organized data science workflows. Learn More - Published: 2025-03-13 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/team-collaboration Team Collaboration Accelerate innovation with seamless knowledge-sharing capabilities Get a Demo Avoid Duplication of Efforts and Misalignment of Priorities Team collaboration is essential for leveraging expertise, sharing knowledge, and maintaining organized data science workflows. Version Control and Reproducibility Accurately reproduce experiments and validate analyses. Documentation and Sharing Centralize code collaboration, explanations, and visualizations. Environment Management Analyze and experiment in a consistent and reproducible environment. Data Sharing and Security Take a look at all we have to offer. Find marketing resources to generate leads, create. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Additional Resources Why Data Scientists Should Be Excited About Python in Excel Learn More Python in Excel creates a common working platform for data scientists and spreadsheet users, dramatically streamlining the collaboration process How to set up collaboration on projects Workbench tracks all changes to a project and lets you know when files have been updated. Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to... --- > Anaconda's machine learning capabilities encompass a diverse array of algorithms and tools, empowering users to build, train, and deploy sophisticated models. - Published: 2025-03-13 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/machine-learning Machine Learning Develop, train, evaluate, and deploy machine learning models. Get a Demo Streamline with Scalable Resources and Integrated Tools Data Processing Tool Integration Seamlessly integrate with data processing tools like NumPy, SciPy, and pandas. Algorithm Experimentation Use tools like MLflow to select and evaluate models and tune parameters to optimize model performance. Libraries and Tools Access to libraries and tools such as scikit-learn, TensorFlow, PyTorch, and Keras. Scalable Computing Resources Leverage parallelization and distributed computing solutions for training large datasets. Seamless Model Deployment Deploy machine learning models into production environments and monitor their performance in real time. Anaconda Notebooks Start coding immediately with Anaconda Notebooks. Learn More Machine Learning Resources Building an Interactive ML Dashboard in Panel Learn More Shaping Best Practices for Monitoring ML Models Listen On-Demand Introduction to Machine Learning Take the Course Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > On demand infrastructure enables organizations to effectively leverage compute resources and accelerate innovation. - Published: 2025-03-13 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/capability-on-demand-infrastructure On-Demand Infra-structure Manage your data science resources efficiently and save time and money. Get a Demo Data Science Management with On-Demand Infrastructure Performant infrastructure is crucial for data scientists who perform complex data analyses,model training, and machine learning tasks. Scalable Accelerate innovation and agility in your data science projects. Empower your data scientists with rapid model iteration and experimentation. Cost Efficient Simplified model deployment enables you to achieve a faster time-to-market and rapidly deliver value to your stakeholders. Accessible Reduce your dependency on DevOps teams, empower your data scientists to drive progress autonomously. Speed Optimize your model deployment to unlock efficiency and value for your organization effortlessly. Resources The Definitive Guide to AI Platforms for Open-Source Data Science and ML Learn More AI Development in the Enterprise Watch Now Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Anaconda can bridge the gap between your data science and IT teams. Get MLops workflows that drive value from your AI initiatives - Published: 2025-03-13 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/capability/visualization Visualization Efficiently explore, analyze, and communicate complex data for informed decision-making. Get a Demo Derive Insights From Intricate Datasets Anaconda’s data science solutions encompass comprehensive visualization libraries, Jupyter Notebooks integration, dashboarding solutions, and scalability and performance optimizations. Uncover Business Insights Visualization facilitates the discovery of patterns, trends, and outliers within data, thereby enhancing insight generation beyond what is achievable through raw data or numerical summaries. Communicate to Stakeholders Visualizations offer an intuitive and accessible medium for communicating findings and insights to stakeholders, thereby fostering better understanding and informed decision-making. Exploration Interactive visualizations empower users to dynamically explore data, facilitating in-depth analysis from diverse perspectives. Support Effective Decision-Making Visualizations aid decision-making by presenting data in a clear, concise, and actionable format, thereby reducing cognitive load and enhancing decision-making efficiency. Anaconda Notebooks Share visualizations, dashboards, and projects with stakeholders directly from Notebooks using Panel. Learn More Additional Resources Why Data Visualization is One of the Hardest but Most Important Tasks Learn More Introduction to Data Visualization with Python in Excel Learn More Learn how to create visualizations and dashboards Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Anaconda ensures consistency in collaborative environments, managing complex project dependencies and reproducing analysis, and experimental results. Learn More - Published: 2025-03-13 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/capability-version-control Version Control Version control plays a crucial role in overseeing and documenting alterations made to data, code, and models within AI and data science projects Get a Demo Complex Projects Require Consistency Manage Code, Data, and Models Integration with version control systems like Git enable change tracking and easy collaboration. Manage Environments Conda environments ensure reproducible configurations of packages and libraries. Collaborate Effectively Multiple users can work on the same project and quickly resolve conflicting changes. Reproduce Results Integrated tools like Jupyter Notebooks enable users to document their code, methodologies, assumptions, and results, facilitating reproducibility and transparency. Connect to Git Repos Users can also connect to external git repos to use specific git extensions. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Anaconda fosters AI reproducibility by offering version control and dependency management, ensuring consistent environments for model development and deployment - Published: 2025-03-13 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/reproducibility Reproducibility for Data Science Projects Recreate, validate, and ensure reliable and credible data. Get a Demo Innovative and Reproducible Remove barriers to innovation and instill confidence in the integrity of data-driven analyses. Environment Management Ensure analyses and experiments are executed in a consistent and reproducible environment. Version Control Trace the evolution of analytical methods and models to accurately reproduce experiments. Documentation and Collaboration Centralize code collaboration, explanations, and visualizations. Containerization Package code, data, and dependencies into portable containers. Package Consistency Use tested and compatible versions of packages. Resources 8 Levels of Reproducibility: Future-Proofing Your Python Projects Learn More Anaconda Learning: Turbocharge your Python Journey in Anaconda Notebooks Learn More Build and Deploy Data Apps in Anaconda Notebooks Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Secure Open Source Software. Implement robust security tools and practices to protect your organization keeping you on the forefront of technological solutions. - Published: 2025-03-13 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/secure-package-management Secure Package Management Innovate safely with trusted open-source software Get a Demo Secure Your Open Source Software Package Signatures Know that your packages haven’t been tampered with during transit with cryptographic signatures. Curated Packages Trust the professionals to do the tedious work. Get professionally curated packages with a summary for review. Vulnerability Monitoring Keep your channels safe from ever-emerging and changing package vulnerabilities. Risk Mitigation Prevent dangerous or unwanted packages from reaching your workforce. Dependency Management Get the tool of the trade for managing package dependencies, ensuring compatibility between software packages. Package Auditing Take a look at all we have to offer. Find marketing resources to generate leads, Track package usage, identify vulnerabilities, and generate audit logs to ensure compliance. Package Security Manager Tackle the complexities of securing software packages, managing vulnerabilities, creating security policies, and meeting compliance standards using Package Security Manager. Learn More Resources Read more about conda package signature verification. Learn More Secure by Design: How Conda Signature Verification Secures Your Software Pipeline from the Start Watch the Webinar Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- - Published: 2025-03-11 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/partners/channel-and-services Channel and Service Partners Join our network of resellers, distributors, referral partners, and services partners — and foster AI innovation through collaboration Become a Partner Partnership Options Distributor Tap into the world’s largest Python community and enjoy deals and incentives. Approved Reseller Sell directly to Anaconda customers. Access training and support, and share in Anaconda’s success. Premier Reseller Sell to Anaconda customers, and install and provide tier 1 support for Package Security Manager. Partner Marketing Benefits Strategic Co-Marketing Together with your marketing team, we jointly promote our partnership value to the industry, customers, and prospects through webinars, events, and content. Enablement Access branding, collateral, subject matter experts, and other marketing support so you can run tailored demand-gen campaigns and go-to-market efforts. Demand Generation Anaconda promotes partnerships through PR and through campaigns that drive demand. Meet Our Channel and Services Partners What Our Partners Say "Anaconda offers many key benefits for the professional use of data science in enterprises such as security, flexibility and efficiency. With this partnership, we are looking forward to further advancing the development of secure Al and ML solutions in Europe. " Oliver Bracht Chief Data Scientist, Eoda "The mix of tools, technologies and processes aligned with a fantastic Anaconda sales and support team makes it easy to do business and provide customer insights in an agile, affordable, well-structured, fully governed manner. " Kielty Hughes CEO, ISx4 Become a Partner Interested in growing together? Learn More --- > Learn Python, AI, and data science your way. Anaconda Learning offers self-paced courses, professional certifications, and hands-on coding practice. - Published: 2025-03-11 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/learning Anaconda Learning Build data science and AI skills today with your Anaconda account. Browse Catalog Create Account https://anacondasite. wpenginepowered. com/wp-content/uploads/2025/03/PeterWangWelcome-v2. mp4 Learn Your Way Level up your artificial intelligence, data science, and Python skills On-Demand Courses The essentials on getting started with AI, Python, and Anaconda tools. Browse Free Courses Learning Paths A curated collection of premium courses for students, data professionals, and anyone using AI. Browse Learning Paths Certifications Comprehensive certification programs to build your professional and technical skills. Browse Certifications Block-Style Coding Want to learn the basics of programming? Try our free EduBlocks platform, ideal for young learners and teachers. Try EduBlocks Featured Courses Getting Started with Anaconda Take your first steps using Anaconda Distribution, working with conda, and writing your first Python program. Get Started Introduction to Python Programming Learn to read, write, and solve real-life problems with Python. Learn Python Introduction to Machine Learning Get started with fundamental machine learning algorithms using scikit-learn. Learn Machine Learning Build Skills for Success Learn, research, build, and share projects Track Your Progress See how close you are to reaching your learning goals. Develop Your Portfolio Build personalized projects and showcase your success. Earn Completion Certificates to Share Share progress with your network. High-Capacity Cloud Notebooks Build personalized projects and showcase your success. What Our Customers Say “Anaconda does a world-class job. I would almost call Anaconda an education company that has an awesome Python environment, not the other way around. ” Joe Reis Cofounder and CEO of Ternary Data,... --- - Published: 2025-03-10 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/partners/python-in-excel Python in Excel Harness the Power of Python in Microsoft Excel's Familiar Interface Experience Python In Excel Next level data analysis from Anaconda and Microsoft Excel The world’s most popular data application meets the world’s most trusted Python distribution. Do More with your data Use popular Python libraries within Excel for complex data analysis and modeling. Create Custom Visualizations Explore, manipulate, and present data like never before. Streamline Your Data Preparation Simplify complex data transformations and boost productivity while minimizing manual effort. Share Work Effortlessly Your work becomes more accessible, easier to understand, and easier to reproduce. Experience Python in Excel Now Self-Paced Learning Courses Data Analysis with Python in Excel Leverage the power of Python to supercharge your data analysis tasks within Excel. Register Now Machine Learning with Python in Excel Explore the full potential of machine learning using Python and scikit-learn within Excel workbooks. Register Now Python in Excel Resources Blog Posts and Guides What are Python Packages in Excel? 5 Quick Tips: How to get the most out of Excel’s Python integration Python for Excel Analysts: The Basics How to Run Python in Excel on a Mac View All Courses and Certifications What is Anaconda? Introduction to Python Data Analysis with Python in Excel View All Videos Introducing Anaconda Distribution for Python in Excel Python in Excel Tips What Are Python Packages in Excel? View All Discover Next-Level Capabilities with Toolbox Unlock even more capabilities with Python in Excel with the Anaconda Toolbox add-in for Excel Learn... --- - Published: 2025-03-10 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/partners/become-a-partner Become a Partner Tap into our vast network and grow with us Become a Partner Anaconda Partnership Technology Build AI solutions with Anaconda’s open-source Python and R tooling, easily integrated into your platforms and products. Learn More Channel Bring secure, open-source Python and R tooling to customers worldwide as a value-added reseller, distributor, or referral or service partner. Learn More Why Become an Anaconda Partner? Provide a Seamless Experience Enable scaling, security, and productivity in AI and data science work. Drive New Revenue Become a channel partner and benefit from the surge in enterprise AI and ML. Bring Value to Customers Enable scaling, security, and productivity in AI and data science work. Join Our Community Share your content with our community of 45 million users. It’s growing up to 300% year over year and includes 90% of Fortune 500 companies. Partner Resources Resource: Channel Partner Program Guide Learn about partnership tiers, benefits, and onboarding. Download Channel Partner Guide Partner Go-to-Market Kit Get assets, collateral, and guidelines to add Anaconda to your partner directory, enable your sales teams, and engage your audience. Sign Up for a Kit What Our Partners Say "Anaconda offers many key benefits for the professional use of data science in enterprises such as security, flexibility and efficiency. With this partnership, we are looking forward to further advancing the development of secure Al and ML solutions in Europe. " Oliver Bracht Chief Data Scientist, Eoda "The mix of tools, technologies and processes aligned with a fantastic Anaconda sales... --- - Published: 2025-03-10 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/partners Anaconda Partner Network Empowering AI Advancement Together Become a Partner 0 M+ Users our partners can reach 0 M Organizations: 90% of Fortune 500 and 100% of Ivy League 0 + Partnership types: technology, channel, and more Partnership Options Technology Anaconda experts build and optimize Anaconda Repository artifacts tailored to your hardware, working with your team to create and add packages and artifacts to Distribution. Work with Anaconda to embed our technologies, optimize your own, or showcase your offerings through partner placements. Learn More Channels and Services Access secure, open-source Python and R tooling through Anaconda-certified resellers, distributors, referral partners, and services partners who offer consultation and advisory options. Learn More Partner for a Smarter, AI-Driven Future Tap into Anaconda's extensive AI-focused customer network and deliver powerful AI-ready value to your customer base. Learn About Partnership Offerings --- > Explore how Anaconda is transforming industries worldwide, including the fields of healthcare, financial services, government, manufacturing, and technology. - Published: 2025-03-05 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/industries-2 AI and Data Science Solutions to Empower Your Industry Explore how Anaconda is transforming industries worldwide. Get a Demo Trusted by 90% of Fortune 500 Companies Discover Industry-Specific AI Solutions Financial Services Revolutionize financial analytics and security with tailored AI solutions. Learn More Government Transform government operations and citizen engagement with secure data science and AI tools. Learn More Healthcare Transform patient care and healthcare operations with bespoke AI solutions. Learn More Manufacturing Drive efficiency and innovation in your manufacturing processes using advanced AI. Learn More Technology Accelerate growth with the most popular AI and data science tools for developers. Learn More Academia Get free access to AI courses, storage, and tools with your academic email address. Learn More ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Learn how Anaconda ensures digital accessibility for all users. Discover our WCAG 2.1 Level AA compliance and commitment to inclusive design. - Published: 2025-03-05 - Modified: 2025-07-11 - URL: https://10.2.107.56:8443/accessibility Accessibility Commitment Anaconda is committed to ensuring digital accessibility for all users, including individuals with disabilities. We strive to create a website that is accessible, user-friendly, and compliant with the applicable accessibility standards and guidelines. Our website has been designed to conform with the Web Content Accessibility Guidelines (WCAG) 2. 1, Level AA, as well as other relevant standards to promote inclusivity and ease of use for all visitors. We continually monitor our website’s accessibility and aim to address any barriers that may arise in a timely manner. If you encounter any difficulties accessing content or functionality on our site, or if you have suggestions for improving accessibility, please contact User Care. Your feedback is invaluable to ensuring that all users have equal access to our services. Thank you for visiting www. anaconda. com and for supporting our commitment to inclusivity and accessibility. --- > Anaconda enables AI by providing solutions to build applications for risk management, algorithmic trading, and customer insights. Learn more - Published: 2025-03-05 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/industries/technology Trusted AI Tools to Revolutionize Technology Accelerate technology evolution with cutting-edge AI solutions. Download Overview Get a Demo Lead the Tech Industry with AI Agile AI Development AI simplifies development from concept to deployment, enhancing productivity and innovation with comprehensive tools. Scalable Machine Learning Models Build scalable AI models that grow with your needs, ensuring robust solutions for future challenges. Cloud-Native Development Cloud-native development lets teams leverage cloud technologies for AI with maximum flexibility, scalability and performance. Open-Source Innovation AI innovation through open-source packages, providing access to cutting-edge machine learning libraries in a secure framework. Secure Collaboration Safely collaborate on AI development without compromising data security or intellectual property. Enhanced Data Governance AI projects maintain highest data governance standards, managing usage, privacy, and compliance for trustworthy solutions. Professional Services Address manufacturing challenges with Anaconda's expert advice and targeted open-source improvements to propel your processes forward. Learn More Trusted by 90% of Fortune 500 Companies Learn More AI Use Cases for the Enterprise Read spcific use cases of Ai in Tech in this overview of use cases across all industries. Learn More The Definitive Guide to AI Platforms View reference guide about AI platforms, common use cases, and factors to consider when evaluating AI Platforms. Learn More 2025 AI Predictions: Uncover the Future of AI Explore the trends, innovations, and opportunities shaping the future of AI with industry experts. Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap... --- > Explore how Anaconda’s solutions can help enhance efficiency, quality control, and innovation for smarter, more sustainable manufacturing. Learn more - Published: 2025-03-05 - Modified: 2025-07-31 - URL: https://stage.anaconda.com/industries-2/manufacturing Trusted Tools to Revolutionize AI in Manufacturing Elevate production efficiency and innovation Download Overview Get a Demo Empower Public-Sector Innovation Predictive Maintenance Predict equipment failures with real-time data analysis for timely maintenance, reducing downtime and improving efficiency. Quality Control Enhancement AI quality control systems detect defects accurately, elevating product quality while reducing waste to meet customer demands. Supply Chain Optimization AI optimizes supply chains by forecasting demand, managing inventory, and enhancing logistics for smarter decisions. Advanced Process Automation AI automation enhances manufacturing by handling repetitive tasks, boosting productivity for higher-value work. Energy Efficiency AI monitors and optimizes manufacturing energy use, identifying savings opportunities to reduce costs and support sustainability. Customized Manufacturing AI enables efficient customized production by analyzing customer preferences for personalized manufacturing at scale. How it Works Package Security Manager Ensure software integrity with Anaconda Package Security Manager. Mitigate risks and meet regulations with secure packages. Learn More Professional Services Address manufacturing challenges with Anaconda's expert advice and targeted open-source improvements to propel your processes forward. Learn More Trusted by 90% of Fortune 500 Companies Learn More AI Use Cases for the Enterprise Discover how enterprises use AI to innovate products and services while optimizing operations. Download Now The Definitive Guide to AI Platforms Explore open-source tools across industries. Empower teams to build and maintain secure AI solutions. Learn More AI, ML, and Data Science Webinar Explore AI uses in manufacturing and what data scientists should consider when implementing solutions. Watch Now Transform Your Organization's AI Capabilities Today Join... --- > Anaconda for Education offers users with academic emails free access to premium features, including tools, resources, and cloud storage to enhance learning. - Published: 2025-03-05 - Modified: 2025-08-13 - URL: https://stage.anaconda.com/industries-2/education Anaconda for Education: Accelerate Your AI Learning Get free AI courses, storage, and tools — premium features all at no cost, with academic access to the Anaconda Starter plan. Create a free account using your academic email address. Check your eligibility and read our sign-up instructions in our Academic Policy. Sign Up for Free Unlock AI in Higher Education AI Navigator Install and interact with cutting-edge LLMs and other AI models — while working locally, so your data stays secure. Learn More Anaconda Assistant Get help writing, analyzing, and debugging code directly in Notebooks with this AI-powered chatbox. Learn More Notebooks Start coding immediately and share dashboards and projects with stakeholders from the browser. Learn More Anaconda Learning Advance your knowledge and career by learning in-demand skills with a full catalog of on-demand learning courses. Learn More Empowering Users Across Academia Students Manage and share notebooks easily and leverage extra compute for coursework and studies Educators Take a look at all we have to offer. Find marketing resources to generate leads, create Research Scientists Leverage Anaconda for research projects and publications. We want to hear from you Want to help inform the features we build for education? Take our survey What’s Included Products and ToolsBusiness PlanAnaconda DistributionAnaconda NavigatorAnaconda AI AssissantPublic Package RepositoryAnaconda Learning: Full Course CatalogCloud Notebooks with increased storage and computeApp publishing with Panel Anaconda for Education FAQs I already have an Anaconda account with a different email address. How do I get academic access? We recommend that you... --- > Discover developing AI in healthcare with Anaconda to improve patient care, and operational efficiency, and ensure data privacy in the medical field. Learn more - Published: 2025-03-05 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/industries/healthcare Revolutionizing Healthcare with Trusted AI Tools Advance patient care and operational efficiency with AI Download Overview Get a Demo Empowering Healthcare Innovation Improve Patient Outcomes AI analyzes patient data for accurate diagnoses and personalized treatments, helping providers improve care outcomes. Operational Efficiency AI optimizes hospital workflows, reducing wait times and increasing care delivery efficiency for seamless operations. Enhanced Diagnostic Accuracy AI tools enhance diagnostic accuracy through advanced imaging analysis to support clinical decisions. Predictive Analysis AI predicts public health trends to anticipate outbreaks, manage resources, and plan preventive measures. Data Security and Compliance AI ensures data security and regulatory compliance when handling patient information to protect privacy. Accelerate Research and Development AI accelerates medical research to speed up discovery of new treatments and medical advancements. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Trusted by 90% of Fortune 500 Companies Additional Resources The Total Economic Impact of Anaconda Report Study shows Anaconda AI Platform delivers significant cost savings and efficiency Read Report The AI Imperatives of 2025 Three key AI imperatives and risks for organizations looking to thrive in this new... --- > Anaconda enables AI for financial services and banks by providing solutions to build applications for risk management, algorithmic trading, and customer insights. Learn more - Published: 2025-03-05 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/industries/financial-services Trusted Tools to Build AI in Financial Services and Banking Unlock efficiency, innovation, and informed decision making with AI built on the world’s most trusted data science platform. Download Overview Get a Demo Transform Operations and Customer Experience Enhance Risk Management Analyze financial data in real-time to enhance risk management, detect fraud earlier, and ensure compliance. Revolutionize Trading Strategies Develop algorithmic trading models for faster, data-driven decisions that boost performance and profitability. Advanced Fraud Detection Create AI fraud detection systems that adapt to new threats, preventing losses and protecting reputation. Accelerate Financial Analysis Process large datasets quickly to unlock faster insights for strategic decisions and competitive advantage. Improve Customer Insights AI-powered customer insights predict behavior, personalize offers, and improve service for increased loyalty. Simplify Regulatory Compliance Automate compliance reporting to navigate complex regulations, reduce errors, and maintain trust. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Additional Resources The Total Economic Impact of Anaconda Report Study shows Anaconda AI Platform delivers significant cost savings and efficiency Read Report The AI Imperatives of 2025 Three key AI imperatives and risks for organizations... --- > Explore AI solutions for government agencies to enhance public services, ensure data security, and drive operational efficiency with Anaconda. - Published: 2025-03-05 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/industries/government Trusted Tools for AI in Government Services Innovate public services with secure AI solutions. Download Overview Get a Demo Empower Public-Sector Innovation Enhanced Public Services Use AI to enhance public services. Deliver responsive, personalized solutions that improve citizen satisfaction. Efficient Resource Management AI optimizes resources. Anaconda helps manage public assets effectively, maximizing impact. Data-Driven Decision Making Empower decision makers with AI-driven insights from large datasets, ensuring data-backed policies rather than intuition-based choices. Secure Data Practices AI solutions with highest security standards protect sensitive government data against cyber threats. Accessibility and Inclusion AI breaks down barriers to make government services accessible to all citizens, including those with disabilities. Fraud Detection and Prevention AI systems detect government program fraud, identifying irregularities to protect public funds and maintain trust. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Trusted by 90% of Fortune 500 Companies Additional Resources The Total Economic Impact of Anaconda Report Study shows Anaconda AI Platform delivers significant cost savings and efficiency Read Report The AI Imperatives of 2025 Three key AI imperatives and risks for organizations looking to thrive in this... --- > APIs integrate AI models into applications, web services, and IT infrastructure and let developers rapidly develop and deploy, protect data, and collaborate. - Published: 2025-03-04 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/capability-deply-inference-apis Deploy Inference APIs Allow your AI models to communicate with the world. Get a Demo Inference APIs Support to rapidly develop and deploy models, protect data, and collaborate with stakeholders Rapid Development and Deployment Focus on value-adding features instead of complex AI models to speed up product launches. Seamless Integration Seamlessly integrate machine learning models into existing applications, enabling these systems to directly work together. Scalability Connect multiple applications and users to the same model, maximizing the impact of your data science investments. Stakeholder Collaboration Create a shared interface for teams to access and use models, promoting experimentation and fast iteration. Enhanced Security and Control Implement security measures like authentication, encryption, and usage limits from a controlled access point. Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Anaconda offers the all-in-one data management solution, integrating acquisition, analysis, and collaboration through a comprehensive suite of tools and libraries. - Published: 2025-03-04 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/capability-data-management Seamless DataManagement Solutions Sort, assess, and clean data with ease Get a Demo Drive Clear Insights with Organized Data The all-in-one data management solution Data Acquisition Easily consolidate data from diverse sources into a central location. Data Organization Clean, transform, and analyze structured data. Data Integration Merge, join, or pivot datasets from varying sources. Data Storage Store data in various formats with distributed system support. Data Analysis Execute various data analytics tasks: stats, modeling, clustering, and beyond. Data Visualization Create insightful visualizations for comprehensive data interpretation. Environment Management Set up isolated environments tailored for seamless workflow transfer. Related Products Package Security Manager Ensure software integrity with Anaconda Package Security Manager. Mitigate risks and meet regulations with secure packages. Learn More Conda Anaconda comes with conda, a powerful package and environment manager. Learn More Additional Resources Data Science & AI Workbench: all-in-one solution for data science Learn More Building an OSS Governance Program for ML and AI Watch the Webinar Learn How to Load Data into Your Project in Workbench Read the Docs Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver ROI for your organization. Get a Demo --- > Anaconda empowers data scientists and analysts to effortlessly deploy captivating, interactive dashboards using Panel. Learn More - Published: 2025-03-04 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/capabilities/dashboards Elevate Your Dashboards Anaconda empowers data scientists and analysts to effortlessly deploy captivating, interactive dashboards using Panel. Get a Demo Simplify Dashboard Creation Anaconda simplifies complex dashboard deployment challenges Seamless Integration Transform models into interactive dashboards with Panel's seamless Anaconda ecosystem integration. One-Click Deployment Take a look at all we have to offer. Find marketing resources to generate leads, create Real-Time Data Support Take a look at all we have to offer. Find marketing resources to generate leads, create Interactive Dashboards Take a look at all we have to offer. Find marketing resources to generate leads, create Anaconda Notebooks A cloud-based environment to host, manage, and share projects. Includes sample projects and data sets to get started quickly. Tell Us About Your Project ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Additional Resources Build and Deploy Data Apps in Anaconda Learn More Build Interactive ML Dashboard in Panel Learn More Panel Core Concepts Learn More Visualizations and Dashboards Tutorial Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap... --- > With Anaconda Assistant, an AI-powered chatbot, get help writing, analyzing, and debugging code directly in Notebooks. Learn More - Published: 2025-03-03 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/capability/anaconda-assistant Anaconda Assistant With Anaconda Assistant, an AI-powered chatbot, get help writing, analyzing, and debugging code directly in Notebooks. Create Cloud Account Get a Demo Do It All With Your AI Assistant Quick, Easy Conversations Engage with advanced AI models in your Notebook - ask questions, request code examples, or clarify concepts directly. Generate Code that Runs in Your Notebook Get code that runs seamlessly within your environment. Code Explanation and Improvement AI explains code functionality, adds clarifying comments, and suggests optimization improvements. Automated Visualizations AI generates DataFrame plotting code with multiple visualization options for engaging data representation. Insightful Data Analysis AI identifies valuable DataFrame data and explains its significance to help you make informed decisions. Leverage the Anaconda Ecosystem Seamlessly integrate AI Assistant into data science workflows Integration Seamlessly integrate AI Assistant into your data science journey with tools, APIs, and an ecosystem that facilitates collaboration. Model Interpretability Build interpretable ML models with libraries like scikit-learn and TensorFlow. Uncover the ‘why’ behind predictions and boost transparency by providing explanations for insights. Data Quality Management Effortlessly manage data quality with libraries like pandas and NumPy, to tackle missing values, outliers, and inconsistencies, ensuring robust and reliable data. Continuous Learning and Model Retraining Dive into automated workflows for seamless data ingestion, preprocessing, model training, and evaluation. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control... --- > Work effectively and safely in a secure environment with Anaconda air gap capabilities. Install offline, manage packages locally, create custom environments, and securely transfer data. Learn More. - Published: 2025-03-03 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/capability/air-gap Air-Gapped Environment Capabilities Work effectively and safely in a secure environment  Download Spec Sheet Air-Gapping for Security Air-gapped systems demand secure solutions. Anaconda can help. Install Offline Set up your requirements without the need for internet access. Manage Packages Locally Always have local access to your libraries and packages. Create Custom Environments Tailor your environments to your specific needs. Securely Transfer Data Protect sensitive data during transit between systems. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights The Total Economic Impact of Anaconda Report Convert open source complexity into enterprise advantage with $1. 18M in validated benefits over just three years. 119% ROI with an 8-month payback period $840,000 in operational efficiency improvements 60% reduction in security vulnerabilities ($157,000 value) $179,000 in technology cost savings Read Forrester TEI Report Package Security Manager Proactively manage risks and ensure compliance in your data science, machine learning, and AI projects. Learn More Additional Resources Package Security Manager Self-Paced Course Take the Course Air Gap Environment Preparation Learn More Mirroring in an Air-Gapped Environment Learn More Transform Your Organization's AI Capabilities Today Join the 95%... --- > Anaconda’s AI & data science governance allows users to establish clear policies and controls in their data science work. Learn More - Published: 2025-03-03 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/capability/governance AI Governance Capitalize on creativity and AI advancements to increase business value Get a Demo Data Science Needs Transparency and Trust Let Anaconda help your enterprise ensure data integrity, privacy, and clear oversight for your projects. Data Management Maintain a comprehensive record of data provenance to ensure data quality and integrity. Accountability Define clear user and admin roles and permissions for access control and decision making. Efficiency and Effectiveness Define roles and permissions to streamline your workflow and reduce duplication of efforts. Regulatory Compliance Support Use built-in security features, encryption mechanisms, and audit trails to support compliance with regulatory requirements, such as GDPR, HIPAA, and CCPA. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Package Security Manager Proactively manage risks and ensure compliance in your data science, machine learning, and AI projects. Learn More Additional Resources Why OSS Security Should be Your Top Priority Learn More Top 14 Enterprise AI Use Cases in 2025 Learn More Building an OSS Governance Program Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge... --- > Anaconda streamlines app deployment by providing tools for packaging and distributing data science applications, ensuring seamless deployment across different environments and platforms - Published: 2025-03-03 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/application-deployment Application Deployment Simplifying Delivery with One-Click Deployment. Get a Demo Efficiently Deploy Your Applications to Maximize Innovation Anaconda simplifies complex deployment challenges for data science applications Simplified deployment Focus on building and refining applications instead of getting bogged down in deployment logistics. Open source package integration Innovate with data science and machine learning tools that integrate seamlessly with your deployment platform. Enhanced collaboration Effectively work with colleagues and stakeholders with rapid and efficient deployment processes. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Resources Selecting an Enterprise Platform for Python and Open-Source A checklist for choosing the right enterprise solution for your business. Learn More Implementing a Full ML Project Lifecycle Take a look at the lifecycle of a Fraud Detection model all the way through deployment. Learn More Deployments Create deployments for microservices such as visualizations, web applications, REST APIs, or Jupyter Notebooks. Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform... --- > Anaconda's GenAI capabilities leverage advanced ML algorithms to automate and optimize data science workflows, enhancing productivity and accelerating the generation of business insights. Learn More - Published: 2025-03-03 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities/capability-gen-ai Generative AI (GenAI) Capitalize on creativity and AI advancements to increase business value. Get a Demo Transforming ideas into Reality Push the boundaries of innovation Innovate Automate original content creation and unlock limitless creativity in digital art, marketing, entertainment, design, and more. Data Augmentation and Synthesis Craft realistic datasets that mimic real-world statistics, ideal for training ML models amidst constraints. Boost Efficiency Accelerate problem-solving, optimize designs, spark creativity, reduce development times, and improve outcomes. Personalization at Scale Elevate customer engagement and refine user interfaces by creating bespoke content and experiences. Enhance Your Creativity Accelerate idea generation, foster exploration, and gain business insights and solutions. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Additional Resources Anaconda Assistant Brings Generative AI to Cloud Notebooks Learn More Address the Need for Python in Generative AI with IBM watsonx. ai and Anaconda Learn More Data Preparation for Large Language Models self-paced course Take the Course Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo... --- > Anaconda's error tracking capabilities allow users to effectively identify, diagnose, and resolve errors in data science projects. - Published: 2025-03-03 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/capabilities/error-tracking-logging Error Tracking & Logging Effectively identify, diagnose, and resolve errors in data science projects Get a Demo Efficiently View and Trace Errors and Logs Quickly identify and diagnose issues with a centralized and streamlined troubleshooting experience Centralized Logging Capture, record, and store logs, metrics, and exceptions across data science workflows for streamlined analysis in a centralized hub. Monitoring Track errors and exceptions in real-time, promptly identifying and addressing issues. Integration Seamlessly integrate with popular logging and error tracking services to leverage existing tools for efficient error tracking and logging. Automation Streamline processes, reduce manual effort, and boost efficiency in your data science journey. ANACONDA AI PLATFORM A Unified Platform to Accelerate Every AI Journey Streamline your AI with open source through a single platform designed for efficiency, security, governance, and compliance. Secure Governance Enterprise-grade governance with role-based access control that aligns with your compliance frameworks Discover More Trusted Distribution Thousands of vetted Python packages, complete with dependency management and security controls Access Distribution Actionable Insights Comprehensive analytics on package usage, team collaboration, and resource utilization to maximize investment Harness Insights Additional Resources The Ultimate Guide to Open-Source Security with Python & R Learn More Anaconda provides top-tier security practices for your teams. Learn More Conda signature verification ensures rustworthiness and security. Learn More Transform Your Organization's AI Capabilities Today Join the 95% of Fortune 500 companies that trust Anaconda to bridge the gap between open source innovation and enterprise requirements. Schedule a demo to see how our platform can deliver... --- - Published: 2025-02-27 - Modified: 2025-07-02 - URL: https://10.2.107.56:8443/resources/webinar Webinars --- - Published: 2025-02-27 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/resources Resources to Help You Code to the Next Level Latest Resources Anaconda Blog --- > Jumpstart your AI projects, create reproducible results, or fine-tune your security governance program. Anaconda offers a wide range of capabilities to meet your needs. Learn More - Published: 2025-02-27 - Modified: 2025-08-04 - URL: https://stage.anaconda.com/capabilities AI and Data Science Capabilities from Anaconda Jumpstart your AI projects, create reproducible results, and fine-tune your security governance program. Get a Demo Capabilities Air-Gapped Environment Isolate your platform’s computing infrastructure from external networks for security Learn More Anaconda Assistant Chat with an AI assistant in your notebook for help with coding, plotting, dataframe analysis, debugging and more. Learn More Application Deployments Panel integrates with Anaconda's Cloud Notebook service, enabling one-click deployment. Learn More Dashboards Effortlessly deploy and share captivating, interactive dashboards using Panel. Learn More Data Management Data management solution integrating acquisition, analysis, and collaboration tools. Learn More Deploy Inference APIs Anaconda tools simplify Python coding to integrate ML models with software and IT infrastructure. Learn More Error Tracking and Logging Identify and fix data science errors to build robust, reproducible, auditable pipelines. Learn More Gen AI Leverage advanced ML algorithms to create business value using generative AI. Learn More Governance Establish clear governance policies and controls in your data science and AI projects for compliance. Learn More Machine Learning Develop, train and deploy ML models with streamlined resources and integrated tools. Learn More Model Library Access a centralized hub for safely storing, sharing and discovering pre-built models. Learn More On-Demand Infrastructure Efficient infrastructure manages data science sources with scalable, accessible, fast setup. Learn More On-Prem LLMs Take advantage of open-source software to jumpstart your build, then refine and monitor your model while keeping sensitive data private. Learn More Reproducibility Track changes and version control to ensure reproducible, innovative... --- - Published: 2025-02-25 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/press/in-the-news In the News --- - Published: 2025-02-25 - Modified: 2025-03-26 - URL: https://10.2.107.56:8443/newsroom Newsroom In the News See All In the News Press Releases See All Press Releases --- - Published: 2025-02-25 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/press Press Release --- > Sitting at the center of the AI revolution, Anaconda empowers our customers and community with open source. - Published: 2025-02-24 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/about-us Advancing AI with Open Source, at Scale Built on a rich history of open source and Python, Anaconda is the guardian of open innovation, helping users utilize AI to turn ideas into impact. Explore Careers Learn More About AI Platform Millions Rely on Anaconda to Advance Their AI Initiatives 50 M Users Globally 1. 9 M Developers and contributors 1 M+ Global Organizations 95 % Fortune 500 companies Our History Anaconda was founded in 2012 by Peter Wang and Travis Oliphant out of the need to bring Python into business data analytics. The use of Python has since exploded, and it’s the most popular programming language used today. Anaconda now has over 300 full-time employees worldwide and is proud to serve over 40 million users! Our Leadership Team Backed by Visionary Partners Recognized Industry Leadership Additional Resources for Your AI Journey Insights Get the latest news on Anaconda’s products and open-source AI initiatives Visit Newsroom Training Advance your AI, data science, and Python skills with our free courses View Now Content Explore our guides, reports, videos, white papers, events, and more View Now Careers Find a career at Anaconda. See all the open roles across the company View Roles Unlock the Full Potential of Open Source Join the 95% of Fortune 500 companies that leverage the security, speed, and scale of Anaconda’s trusted open source distribution to turn their AI strategy from vision to reality. Schedule a consultation to see how our platform can deliver ROI for your organization. Request... --- - Published: 2025-01-10 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/blogs/technical-notes Technical Notes View All Product Perspectives News Technical Notes --- - Published: 2025-01-10 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/blogs/news News --- - Published: 2025-01-10 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/blogs/perspectives Perspectives --- - Published: 2025-01-08 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/blogs/product Product --- - Published: 2025-01-02 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog Anaconda Blog Featured View All Product Perspectives News Technical Notes Product See All Product Perspectives See All Perspectives News See All News Technical Notes See All Technical Notes Product See All Product Perspectives See All Perspectives News See All News Technical Notes See All Technical Notes Product See All Product Perspectives See All Perspectives News See All News Technical Notes See All Technical Notes --- --- ## Posts > As every March Madness fan knows, athletic talent and coaching are key, but it’s how they come together as a unit that determines a team’s success. Known… - Published: 2025-08-06 - Modified: 2025-08-25 - URL: https://stage.anaconda.com/blog/the-ai-governance-paradox - Categories: Blog As every March Madness fan knows, athletic talent and coaching are key, but it’s how they come together as a unit that determines a team’s su... Why 82% of Companies Think They're Secure—But Aren'tEnterprise AI adoption is accelerating at breakneck speed, creating both unprecedented opportunities and hidden challenges that every technology leader should understand. New research from Anaconda surveying over 300 AI practitioners and decision-makers reveals a critical gap between perception and reality: while 82% of organizations believe they have robust processes to validate Python packages and dependencies for security compliance, nearly 40% still regularly encounter security vulnerabilities in their AI projects. Even more telling: 67% experience deployment delays due to security issues. This isn't just a numbers game—it's a strategic opportunity to bridge the gap between what organizations think they have versus what they actually need to unlock AI's full potential. The Confidence Gap: Where Opportunity Meets RealityOur recent research reveals what forward-thinking leaders are beginning to recognize: there's a significant opportunity in the gap between AI governance policies and actual practice. Organizations are implementing layered security approaches—70% use automated scanning tools, 61% maintain internal package registries, 57% conduct manual reviews—yet security vulnerabilities in open-source components remain the most common risk in AI development. The issue isn't a lack of awareness. Leadership teams are tracking compliance metrics, with 58% measuring adherence to regulations as a key performance indicator. The real opportunity lies deeper: we're applying yesterday's governance frameworks to tomorrow's AI challenges, creating untapped potential for those who get it right. Think of it this way—governance isn't your organizational seatbelt. It's your steering wheel. And right now, the organizations that master integrated governance will have... --- > Anaconda co-founder Peter Wang on our $150M Series C funding and deepening investment in open source AI. See how we're building the future of Python and AI. - Published: 2025-07-30 - Modified: 2025-07-31 - URL: https://stage.anaconda.com/open-source-ai-commitment-series-c-funding - Categories: News Anaconda co-founder Peter Wang on our $150M Series C funding and deepening investment in open source AI. See how we're building the future of Python and AI. When Travis Oliphant and I started Anaconda back in 2012, we had a simple but audacious vision: to transform the world of data analysis and business computing using Python and its powerful open source ecosystem. I’m proud to say that we’ve succeeded beyond our wildest dreams, as Python is now the most popular language for data science and machine learning. Most importantly, Python is the language of AI. To better support this transformation and the millions who depend on us, I'm excited to share that we've raised over $150 million in Series C funding, led by Insight Partners. But more than the milestone itself, I want to talk about what this means for our community and the future we're building together. A Moment of Reflection I'm really proud of the history of open source at Anaconda. We have incubated or helped maintain so many impactful projects, and many contributors to core ecosystem projects have come through our doors. Although we've gone through many growing pains as a company, our dedication to the community is unwavering. The evolution hasn't always been perfect. We've learned important lessons about listening to our community and maintaining close communication that goes both ways. Your feedback has been essential in shaping our path forward, and we're committed to turning your input into tangible improvements. The AI Revolution is Open Source Artificial intelligence is reshaping everything—the experiences we have, the industries we work in, the companies we build. What excites me most is that much of the... --- > Transform your financial institution with AI using unified platforms and proven frameworks. See how banks achieve 119% ROI. Get started today! - Published: 2025-07-29 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/building-ai-powered-financial-services-strategic-guide - Categories: News The financial services landscape is experiencing an unprecedented transformation. By the end of 2025, most financial institutions will likely rely on AI to stay competitive, with 75% of banks with over $100 billion in assets expected to have fully integrated AI strategies. Yet beneath this momentum lies a complex web of challenges that directors of security, data science, and AI must navigate with precision and strategic insight. The Critical Challenges Facing Financial Leaders Financial institutions today face a perfect storm of pressures that demand immediate and strategic responses. The numbers tell a compelling story: AI adoption in finance surged from 45% in 2022 to an expected 85% by 2025, with 60% of companies using AI across multiple business areas. This acceleration is a response to existential business pressures rather than simply driven by technological curiosity. Regulatory Complexity and Compliance Burden: The regulatory environment grows more stringent annually, with compliance costs consuming increasing portions of operational budgets. AI will ease compliance by automating Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, but implementation requires sophisticated governance frameworks that many institutions struggle to establish. Fragmented Technology Ecosystems: Most financial institutions operate on a patchwork of legacy systems, proprietary software, and emerging technologies that don't communicate effectively. This fragmentation creates security vulnerabilities, operational inefficiencies, and prevents the unified data governance essential for AI success. Escalating Cyber Threats and Fraud: 91% of U. S. banks use AI for fraud detection, showcasing its effectiveness in combating financial crime. However, as detection capabilities improve, so... --- > Anaconda announcement of the deprecation of Intel Mac (osx-64) package builds, effective August 15, 2025. - Published: 2025-07-17 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/intel-mac-package-support-end-of-an-era - Categories: Product Intel Mac Package Support: End of an Era As Apple completes its transition to Apple Silicon and the broader Python ecosystem evolves, Anaconda is announcing the deprecation of Intel Mac (osx-64) package builds, effective August 15, 2025. What's Changing Starting August 15, 2025, Anaconda will stop building new packages for Intel Mac computers (osx-64). This change affects users who are currently using Intel Mac packages, whether on actual Intel hardware or Apple Silicon Macs running in emulation mode. Important: All existing Intel Mac packages will remain available in our defaults channels. Your current Python environments will continue working normally. Why Now? This decision reflects several industry-wide changes: Apple's hardware transition: Apple stopped manufacturing Intel-based Macs in June 2023 and recently announced that macOS 27 will not support Intel hardware, marking Apple’s full transition to Apple Silicon hardware. Infrastructure reality: Cloud providers no longer support Intel Mac virtualization, which our package build system requires. Continuing Intel Mac support would compromise our ability to deliver secure, reliable packages for all platforms. Industry standards: Major Python packages including PyTorch and TensorFlow already dropped Intel Mac support in 2024. We're following established precedent in the Python ecosystem. What You Need to Do Follow these steps to determine if you need to take action: Step 1: Check your hardware Click the Apple menu → "About This Mac" Look at the processor or chip information: Intel processor = You're running on Intel hardware Apple M1/M2/M3/M4 = You have Apple Silicon hardware   Step 2: Check your... --- > Learn to build scikit-learn models with Snowpark for Python. Clean data, engineer features, and deploy predictions directly in Snowflake using UDFs. - Published: 2025-07-12 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/scikit-learn-model-using-snowpark - Categories: Technical Notes In this article I'm going to demonstrate how to use Snowflake Snowpark for Python to clean data, engineer the features, and train a scikit-learn model. Then I'll build a user-defined function (UDF) using the model to make predictions directly in Snowflake over a large data set. Setup Snowpark for Python is currently in private preview. To request access, click here. To begin with, I need to create a conda environment using packages from the Snowflake channel to ensure compatibility between my local Python environment and the Snowpark environment. Since we're going to be building a scikit-learn model and persisting it as a file, it is important to ensure the exact same version of scikit-learn is used both on my local environment and in Snowpark. conda create -n bikeshare-snowpark python=3. 8 notebook pandas matplotlib scikit-learn=1. 0. 2 And then finally install the Snowpark for Python package using pip. conda activate bikeshare-snowpark pip install snowflake_snowpark_python-0. 6. 0-py3-none-any. whl Data Sets and Modeling Need In this article, I provide a model that the director of the municipal bikeshare system can use to predict the total number of rides given forecasted weather conditions, current month, and whether today is a holiday or weekend. This model can be very useful to the director for capacity planning of the bikeshare system. HealthyRide is the Pittsburgh bikeshare program. Data for all rides between 2016 and 2017 is available at https://healthyridepgh. com/data/. This data set has been loaded into Snowflake as `all_rides`. Daily weather data in Pittsburgh was... --- > Learn about the conda-anaconda-tos plugin that keeps you informed about Anaconda's Terms of Service changes and ensures seamless ToS acceptance in your conda workflow. Open source and CI-friendly. - Published: 2025-07-10 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/using-anaconda-without-worry-enabling-tos-compliance-with-the-conda-anaconda-tos-plugin - Categories: News Accepting the ToS in continuous integration (CI) environments: If you encounter prompts in your CI environment, you can set the CONDA_PLUGINS_AUTO_ACCEPT_TOS environment variable to "yes" to accept the Terms of Service automatically and continue your builds without interruption. As one of Anaconda's millions of users, naturally you want to know when you're using open source with Anaconda and how to use it worry free. Previously, if Anaconda's Terms of Service (ToS) changed after installation, users weren't automatically notified or prompted to accept the updates. To solve that concern, we're introducing the conda-anaconda-tos plugin, a command line tool that ensures you stay informed about ToS changes and requirements throughout your conda workflow while also facilitating ToS acceptance. What does the plugin do? The conda-anaconda-tos plugin integrates with conda to notify users when they're accessing Anaconda channels or packages covered by our Terms of Service (ToS). It automatically works with common conda commands like create, search, and install, showing a clear prompt when ToS acceptance is required. The plugin activates when users access Anaconda's ToS-governed resources, letting normal conda operations proceed without interruption. When a user first attempts to access an Anaconda channel or package covered by our ToS, the plugin will prompt the user to accept or reject the ToS in the command line. If the user accepts the ToS, the user can proceed and a record of that acceptance is sent to Anaconda. If the user rejects the ToS, they cannot proceed with accessing the channel or package. This creates... --- - Published: 2025-07-09 - Modified: 2025-07-09 - URL: https://10.2.107.56:8443/blog/anaconda-recognized-for-excellence-in-ai-innovation - Categories: News We’re thrilled to share that Anaconda has been named a winner in the 2025 Artificial Intelligence Excellence Awards! The Business Intelligence Group has recognized our platform in the Human-machine interaction – Product category, highlighting our commitment to innovation in the AI space. Democratizing AI Through Open SourceAt Anaconda, we’ve always believed that the power of AI should be accessible to everyone. Our platform is designed to simplify the discovery, evaluation, and deployment of AI models, helping users navigate the often complex open-source AI ecosystem with confidence. What makes our solution stand out is its intuitive interface that empowers users of all skill levels to find, customize, and deploy models securely. This approach has significantly accelerated AI adoption in enterprise environments, making advanced technology more accessible to a broader audience. Jane Kim, Anaconda’s Chief Commercial Officer, expressed her thoughts on this recognition: “We’re truly honored to receive this recognition from the Business Intelligence Group. This award affirms what drives us every day – advancing open source as the foundation of innovation. At Anaconda, we’re focused on creating solutions that help builders and organizations bring their ideas to life while fostering a community where collaboration and creativity can flourish. We’re proud to be part of making powerful tools more accessible so everyone can turn their vision into reality. ”An Award That Recognizes InnovationThe Artificial Intelligence Excellence Awards celebrate organizations and individuals who are leading the way in AI innovation. Winners are carefully selected by a panel of industry experts who evaluate nominees... --- - Published: 2025-07-06 - Modified: 2025-07-08 - URL: https://10.2.107.56:8443/blog/evaluating-small-ai-models - Categories: Technical Notes In an era where massive language models dominate headlines, a fascinating trend is emerging: highly specialized smaller models are proving surprisingly capable. But just how capable are they? At Anaconda, we’re committed to helping data scientists and engineers find the right tools for each job. As part of our broader mission to empower data practitioners with reliable open source solutions, we’ve been exploring the performance boundaries of specialized models. This research directly informs our strategy for tools like Lumen AI, where optimizing AI capabilities across different model sizes and specialties is crucial. A year ago, Motherduck released a 7B parameter open-source model focused solely on SQL generation. DuckDB has been gaining significant traction, finding its way into diverse applications from data analysis pipelines to AI platforms like our own Lumen AI. This model was advertised to write DuckDB native queries without the computational overhead of larger models. This raises some intriguing questions: How does this specialized model stack up against much bigger models like GPT-4o? And given the rapid pace of AI advancement, where does it stand compared to newer 7B models like Qwen-2. 5-Coder and the buzzworthy Deepseek R1 Distilled? Explore the full code and project in Anaconda Notebooks: Experiment SetupDatasetTo put these models to the test, the experiment utilized the common penguins. csv dataset that’s widely available online. Using Claude Sonnet 3. 5, we generated 20 question and query pairs about the dataset for evaluation purposes. EvaluatorsWhen implementing the evaluations using Arize, it was not long before we... --- - Published: 2025-07-02 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/shadow-ai-crisis-in-the-enterprise - Categories: Perspectives Survival instinct is driving AI application development at unprecedented rates. As major firms like PwC cut 1,500 jobs while pouring billions into AI platforms, employees aren’t sitting around waiting for IT approval. They’re building custom Python tools, personal copilots, and automated workflows that live completely outside official channels. The numbers tell a story that should make every security leader pause. A recent VentureBeat investigation revealed that consulting firms alone are running an estimated 74,500 shadow AI applications—and that number could hit 160,000 by mid-2026. This parallel tech stack powers real business value while creating blind spots that keep CISOs up at night. The Reality Behind the Numbers Let’s be honest about what’s happening. Cyberhaven’s analysis of 3 million employees found that 73. 8% of workplace ChatGPT accounts were personal, not corporate. That means nearly three out of four AI interactions happen where security teams can’t see them. This isn’t about rogue employees breaking rules—it’s about a fundamental mismatch between how fast people need to work and how long governance takes. According to VentureBeat’s reporting, while IT teams juggle project backlogs that are 3-5 times larger than what they can complete in a year, business teams are solving problems with whatever tools they can find. The enterprise implications are real: Data wandering: People are feeding proprietary information into unvetted AI models without thinking twice Compliance gaps: Shadow tools sidestep every governance framework you’ve carefully built Visibility problems: Your security tools weren’t designed to catch AI-specific risks Talent challenges: Ban AI outright,... --- > The Community Channel—a new addition to the Anaconda AI Platform that expands your organization’s access to more open source packages while preserving the governance and security controls your organization depends on. - Published: 2025-07-02 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/introducing-the-anaconda-community-channel-expanding-your-open-source-arsenal-while-maintaining-enterprise-control - Categories: News Recently, we launched the Community Channel—a new addition to the Anaconda AI Platform that expands your organization’s access to more open source packages while preserving the governance and security controls your organization depends on. The Foundation: Anaconda’s Trusted Distribution For over a decade, Anaconda has hosted the world’s most trusted distribution of conda packages, serving more than 50 million users worldwide with over 4,000 carefully curated and verified packages. The Anaconda Distribution has supported data science and AI development across industries by offering vetted, enterprise-grade packages that organizations can trust. But we know that innovation moves fast, and your teams need access to the broader open source ecosystem to stay competitive. The Power of Conda Packages for Enterprises Conda provides robust dependency management across platforms. Features like environment isolation and cross-language support make conda a strong fit for enterprise use, especially when consistency, reproducibility, and security are key priorities. Today, there are two primary sources of conda packages: Anaconda’s Trusted Distribution, which is built and maintained by engineers at Anaconda and the conda-forge community repository. Conda-forge is an open-source project where contributors from around the world build and maintain packages for the broader conda ecosystem. While packages from conda-forge offer great potential to organizations, enterprise adoption has traditionally required organizations to navigate complex tradeoffs, including different build and maintenance policies and a lack of centralized oversight, which can lead to compatibility issues and security posture gaps. Users should exercise caution when combining packages from Anaconda channels with packages from community... --- - Published: 2025-06-23 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/new-release-anaconda-distribution-2025-06 - Categories: News We are excited to announce the 2025. 06 release of the Anaconda Distribution installer, which includes: Python - the most widely used programming language for AI, data science and machine learning conda - the open-source, cross-platform package and environment manager Anaconda Navigator - our desktop application, built on conda, that enables you to launch notebooks and development applications from your managed environments And over 300 additional, automatically-installed packages that have been tested together to work "out of the box" Anaconda Distribution is free to download, easy to install, and has an active community forum where users help each other troubleshoot issues. The installer is pre-configured to access Anaconda's public package repositories that include over 33,000 AI, data science, and machine learning packages across five different platforms. Download Anaconda Distribution 2025. 06 today and read the release notes for the full list of user-facing changes and packages in this release. The Anaconda Distribution installer is subject to Anaconda's Terms of Service. Python 3. 13. 5 Anaconda Distribution 2025. 06 ships with Python 3. 13. 5. Package Updates Python 3. 13. 5 is included in the base environment, and key package updates include: Dask 2025. 2. 0 Jupyterlab 4. 3. 4 Matplotlib 3. 10. 0 Numba 0. 61. 0 Numpy 2. 1. 3 Pandas 2. 2. 3 Scikit-Image 0. 25. 0 Scikit-Learn 1. 6. 1 SciPy 1. 15. 3 Spyder 6. 0. 7 See the complete package lists, and explore our Anaconda Distribution 2025. 06 metapackages, with builds for Python 3. 9,... --- - Published: 2025-06-16 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/international-womens-day-2021 - Categories: Perspectives - Tags: news In honor of International Women’s Day, I want to tell you about my experience as a woman in tech and how both female and male leaders have positively impacted my career. Over the past decade of working in HR and operations from startup environments to Fortune 50, I’m proud to be a part of a changing culture that bridges the barriers for women in the workforce. Why is there a leadership gap? When I started working on senior leadership teams in my career, I found myself the only woman at the table, often due to traditional woman leadership roles only being in HR or marketing. It was challenging enough to get a word in as a woman and also as an HR professional at that time. During meaningful discussions, men would talk over me or say the same thing I was saying that was dismissed moments before. I felt out of place and found myself overcompensating because the environment made me insecure about my work. This sentiment isn’t uncommon for women in leadership roles and has been exacerbated by the COVID-19 pandemic. With virtual work, school, and childcare all happening under one roof, research shows women are doing more housework and chores than men during this time. And with this, mothers are twice as likely to worry about their work performance due to their home responsibilities. A lack of support and the pressure to meet the same expectations as senior-male counterparts is causing burnout and leading women to leave their... --- > Anaconda Toolbox is a Microsoft Excel add-in that brings AI-powered Anaconda Assistant, curated data catalogs, and cloud features to Python in Excel users. - Published: 2025-06-09 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/blog/how-anaconda-and-databricks-are-solving-enterprise-ai-biggest-open-source-challenge - Categories: News Opening HookAI is moving fast, but enterprise adoption often gets stuck in the same old bottlenecks: dependency hell, ungoverned open-source packages, and compliance risks that slow down innovation. For many organizations, deploying secure, reproducible, and scalable AI models in production is still a major challenge. With billions invested in AI platforms, organizations can’t afford to let governance and reproducibility issues stall progress. That’s why the recent native integration between Anaconda and Databricks is such a game-changer: it brings the best of open-source Python and enterprise-grade security into one seamless experience. In a pivotal move for enterprise AI development, Anaconda, the leader in advancing AI with open source for its 50+ million users, is proud to announce a strategic partnership and native integration with Databricks, the data and AI company known for its industry-leading data intelligence platform. This collaboration merges Anaconda’s secure, curated open-source Python packages with the scale, performance, and governance of Databricks’ unified analytics and machine learning platform. It represents a new standard for enterprise-ready AI development—reducing friction, accelerating time-to-value, and ensuring compliance at scale. This partnership marks the first time that Anaconda’s enterprise-grade Python ecosystem is available natively within Databricks Runtime, making it easier than ever for joint customers to develop, deploy, and scale AI/ML solutions with confidence. Key Context and BackgroundOver the past decade, open-source Python libraries have become the de facto standard for data science, machine learning, and now generative AI. Yet, even as they’ve enabled massive experimentation and innovation, they’ve also created new risks around... --- > Streamline access to Anaconda Premium repositories with our new CLI-based token management workflow. Install, configure, and authenticate with a single command - anaconda token install. - Published: 2025-06-04 - Modified: 2025-07-02 - URL: https://10.2.107.56:8443/blog/introducing-anaconda-auth - Categories: News Introduction Simplify your access to premium repositories with Anaconda's new CLI-based token management workflow. We've completely reimagined how Business Tier customers interact with the Anaconda Premium repositories and your organization's hosted channels within the Anaconda AI Platform. The streamlined token management allows both administrators and practitioners to access critical resources with fewer steps and greater security. To see this new workflow process in action, check out the video below or read along for full instructions. A Bit Of Context Before we dive into the new token workflow, it's useful to understand the existing, or now legacy but still supported, workflow for obtaining and using an Anaconda token. Previously, in order to obtain a token, users had to log into the Anaconda. com portal, navigate to their organization's page, then navigate to the "Token Access" section, and click the "Reissue token" button. After clicking the button the user received an email with further instructions on how to install the token by copying and pasting several commands into their terminal, and then finally read the Anaconda Quickstart Guide to learn how to configure their . condarc file. Obviously, this wasn't an ideal scenario and many folks expressed dissatisfaction with the number of steps they were required to go through to get and use a token. Now we've introduced a new workflow that meets users where they are, on the command line. This new workflow provides a single command that obtains a token, installs the token, and configures the user's . condarc file... --- - Published: 2025-05-29 - Modified: 2025-07-12 - URL: https://10.2.107.56:8443/blog/letter-to-our-community - Categories: Perspectives Dear Anaconda Community, I want to address you directly regarding the changes and growth Anaconda has experienced and the updated Terms of Service (ToS) changes we're implementing. But first, I owe you - our valued community members - an acknowledgment. Last year, eager to continue iterating on our offerings and keep up with our growing user base, we rolled out not only updates to our ToS but also operational changes to the way we enforce them. We know this took many by surprise, particularly our academic and research users. The confusion, frustration, and disappointment this caused within our community is something we regret. We should have done better.   Our intent with these ToS updates was to ensure that we can continue offering great benefits to our community for years to come. Our investments in open source innovation help power research institutions and academics worldwide. These investments have been, and will continue to be, financially sponsored by Anaconda. They are not free, but they are important. These investments are core to Anaconda’s mission. And to continue these investments and Anaconda’s contributions to innovation across the broader Python community, particularly in the age of AI, we have to make sure that Anaconda’s proprietary intellectual property is not being abused by for-profit enterprises. We’ve reflected on the past experience of our prior rollout of our ToS updates and have spent considerable time listening to your feedback, meeting with academic and research groups, reflecting on our approach, and rethinking how we structure and... --- - Published: 2025-05-21 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/blog/anaconda-assistant-for-conda-private-beta - Categories: News You may already be familiar with the Anaconda Assistant, the AI coding assistant from Anaconda Toolbox, available on https://nb. anaconda. cloud and locally using Anaconda Navigator. Today we are pleased to bring our AI-powered assistant to all conda users with the anaconda-assistant-conda plugin. This private beta release of Anaconda Assistant for conda summarizes the error message with recommendations for correcting the problem. The full documentation is available on the Github repository. You can check out a full demo of the Anaconda Assistant in action by watching the video below. Interested in participating in the Anaconda Assistant for conda private beta? Apply here!   Note that in order to use the Anaconda Assistant for conda you will be asked to: consent to our terms of service and privacy policy, and to opt-in or opt-out of data collection for the requests you make Installation and Authentication To utilize the Anaconda Assistant for conda plugin, you will want to install it into your base environment so conda can use it from any environment. conda install --name base --channel anaconda-cloud anaconda-assistant-conda You can verify installation by running: conda list anaconda If installation was successful, you’ll see anaconda-assistant-conda listed. Error messages While using conda in your workflows you may have encountered error messages like the following. For example you may have seen something like: Or maybe an error like this: With the Anaconda Assistant for conda installed the Anaconda Assistant will intercept the error message, analyze it, and recommend steps to correct the error. Let’s... --- - Published: 2025-05-19 - Modified: 2025-07-02 - URL: https://10.2.107.56:8443/blog/gratitude-and-growth-reflecting-on-2023-and-embracing-the-promise-of-2024 - Categories: News As the holiday season draws near, we at Anaconda take a moment to reflect on the past year with profound gratitude and look ahead to 2024 with excitement and anticipation. 2023 marked a year of significant achievements for us. We’ve enriched our product suite with innovative enhancements in Anaconda Pro, Anaconda Business, and our Data Science Platform. The expansion of the Anaconda Distribution to encompass the latest AI and data science packages, coupled with advanced features in Anaconda Notebooks, new collaboration tools, and the upcoming integration of Python in Excel with Microsoft, along with the Anaconda Extended repository release, are just some of the milestones that punctuated our year. On the open-source front, conda’s elevation to an official NumFocus-sponsored project has broadened its contribution scope, further enriched by substantial functionality and performance improvements. The integration of the libmamba solver into conda’s default install and our collaborative efforts with numba, numpy, and partners like PyTorch, underscore our commitment to the open-source ecosystem. The dynamic growth in data science, machine learning, and probabilistic AI is reshaping the business world. Keeping pace with this rapid evolution is essential, and we are proud to lead the charge, empowering businesses and individuals in their technology journeys. As we look toward 2024, our strategy is anchored in three pivotal themes: strengthening foundations, expanding the operating system (OS) for AI, and empowering high-performance Python. This vision embodies our dedication to delivering a seamless, secure, and high-performance experience to the global AI and machine learning community. Solid Foundations... --- - Published: 2025-05-16 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/blog/sustaining-and-advancing-jupyter-nbclassic-1-3-and-jupyter-fsspec-0-4 - Categories: Technical Notes In today's AI landscape, accessible and powerful tools can define anyone's ability to learn, innovate, and deliver value whether you're a student, independent researcher, or part of a large enterprise. As part of Anaconda's ongoing commitment to open source software investment, which we highlighted in our recent blog post, we've continued enhancing Jupyter capabilities that benefit the community while also addressing specific needs across the spectrum of users. What is Jupyter and Why Does It Matter? Jupyter is an open source interactive computing platform enabling users to create and share documents containing live code, equations, visualizations, and explanatory text. It has become the standard foundation for collaborative data science, powering everything from exploratory analysis to production machine learning pipelines across thousands of organizations worldwide. Jupyter represents more than just a coding tool, it's a strategic platform that enables: Learning and exploration for students and educators Faster innovation cycles for researchers and practitioners Knowledge sharing and collaboration across communities and teams Consistent, reproducible workflows for everyone from independent researchers to regulated industries Unified experiences that democratize access to computing resources Advancing Jupyter for All Users: Recent Updates and Their Impact Data scientists and learners at all levels typically grapple with accessing data across diverse environments. Whether you're a student working between personal storage and university systems, a researcher collaborating across institutions, or an enterprise managing sensitive information, data fragmentation complicates workflows and slows productivity. Our recent updates to two critical Jupyter extensions directly address these needs for stable, secure environments for... --- - Published: 2025-05-13 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/blog/quick-start-enviornments-simplifying-ai-development-for-practitioners - Categories: Guides As data scientists, ML engineers, and developers, we've all faced the frustration of environment setup. The hours spent configuring dependencies, resolving conflicts, and ensuring everything works correctly are hours not spent on actual analysis and model development. Anaconda's new quick start environments aim to eliminate this pain point entirely. What Are Quick Start Environments? Quick start environments are pre-configured, purpose-built environments for Jupyter Notebooks in the Anaconda AI Platform that come with carefully selected packages tailored for specific use cases. Our experts have carefully curated each environment to include the optimal mix of tested, compatible packages, eliminating the complexity that often derails projects and allowing you to leverage configurations that just work. Think of them as expert-assembled toolkits, ready to use the moment you need them, with all components guaranteed to work together seamlessly. Check out the video below to see quick start environments in action, and read on to learn more about the four quick start environments included in this initial release.   How to Access Quick Start Environments Accessing Quick Start Environments is straightforward within the Anaconda AI Platform: Install Anaconda Toolbox from Navigator (or using conda install anaconda-toolbox), or update to v4. 20+ (conda update anaconda-toolbox) if you have it installed already. Log into the Anaconda AI Platform Open Jupyter Lab or Notebooks Open the Anaconda Toolbox and select the “Create New Environment” option.   Launch your chosen environment with a single click The entire process takes minutes rather than the hours often required to configure environments... --- > The Anaconda AI Platform unifies capabilities our users already know and love to help streamline their organization’s AI and data science initiatives with open source. Learn More - Published: 2025-05-13 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/introducing-the-anaconda-ai-platform - Categories: Product Why a Unified Platform, Why Now? The AI landscape has reached a critical inflection point with Gartner research showing over 80% of enterprises will deploy generative AI applications by 2026. However, organizations with fragmented approaches face higher costs and diminished business impact—a gap that's not just technological but fundamentally about delivering measurable value. In today's technological climate, enterprises face three converging challenges. Let me break them down for you: The Productivity Imperative: According to McKinsey Global Institute research, Generative AI could enable up to a 43% increase in the overall productivity growth rate through 2040, creating a widening divide between digital leaders and laggards. With labor force growth slowing, productivity—especially via digitization and AI—has become the key driver of long-term competitiveness. The Expanding Risk Surface: As AI adoption accelerates, the attack surface for open source software is expanding exponentially. By conservative estimates, the risk surface for open source packages has increased nearly 10,000-fold since the early 2020s. Security can no longer be an afterthought—it must be embedded into every aspect of the AI development lifecycle. The ROI Reality Check: Despite growing AI adoption — with McKinsey's State of AI survey showing 78% of companies using artificial intelligence in at least one function as of early 2025, up from 55% in 2023 — the ROI reality remains challenging. From these efforts, companies typically report cost savings of less than 10% and revenue increases of less than 5%. Organizations need solutions that deliver more substantial returns by addressing real-world bottlenecks in the... --- > Anaconda is Free for Academic, Research & Non-Profit Users. Learn more. - Published: 2025-05-13 - Modified: 2025-07-11 - URL: https://10.2.107.56:8443/blog/anaconda-is-free-for-academic-research-and-non-profit-users - Categories: News At Anaconda, we're committed to advancing AI with open source at scale. As part of this commitment, we're revamping our Terms of Service to clarify that eligible academic institutions, researchers, and non-profit organizations can continue to access Anaconda for free. We recognize that our previous Terms of Service update in 2024 created confusion. After gathering considerable valuable community feedback, we're implementing clear policies that better support open source innovation while maintaining our sustainable business model. The full Terms of Service changes will be announced in late May, taking effect July 1st, 2025. Here's what you need to know now: Academic Institution Policy Use of Anaconda’s tools by academic institutions and researchers helps nourish key innovation, and we're committed to supporting this vital ecosystem. As part of our updated Terms of Service, we're rolling out an Academic Policy that confirms organizations meeting the definition of an Eligible Academic Institution will be able to continue to use Anaconda for free. We’ve also launched Anaconda for Education, a dedicated offering designed to support the needs we’ve heard directly from educators and students. With these changes, we're simplifying the process to provide you with confidence and clarity about your eligibility. We value our academic community tremendously and want to ensure there are clear, straightforward paths for you to confidently use our platform without cost barriers.   Who qualifies: Higher education organizations that are accredited  How to participate: Individual academics: Sign up with your . edu email for a free account and follow the prompted... --- - Published: 2025-05-01 - Modified: 2025-07-06 - URL: https://10.2.107.56:8443/blog/navigate-open-source-ai - Categories: Perspectives AI adoption has reached critical mass: 99% of organizations are either using AI or actively exploring it, with nearly half in the scaling phase, according to Enterprise Strategy Group research. Yet alongside this growth, risks persist: hallucinations, unscalable deployments, shadow AI, and security vulnerabilities that threaten even promising projects. “Enterprises need to be really smart about AI adoption because they will sleep-walk themselves into a massive amount of regulatory and implementation hurdles,” said Peter Wang, Anaconda’s co-founder and chief AI officer, during a recent webinar with ESG’s principal analyst, Mark Beccue. In this blog, I explore key takeaways from their conversation and share insights from our recent “2025 State of Enterprise Open Source AI” report. Controlling Your AI Destiny and the Promise of Open Source  “Enterprises are adopting open-weight models for good reason,” Wang said. “They want to control their own destiny. ” What did Wang mean by open-weight models? Well, when most companies talk about open source AI, what they actually mean is open-weight models – pre-trained, publicly available AI models that anyone can download and use.   “Open source models should include the training data that was used to train the model, and nobody wants to talk about their training data. Nobody,” Wang emphasized.   While open-weight models conveniently allow organizations to start harnessing the power of AI, they still represent a “black box” system because the training data isn’t available, preventing organizations from fully interpreting how it processes information or produces outputs. Without proper model interpretability, you’re essentially flying... --- - Published: 2025-04-24 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/anaconda-gartner-voice-of-the-customer-report - Categories: Perspectives The data science and machine learning (DSML) landscape continues to evolve at a rapid pace, with organizations facing increasingly complex challenges in managing models, data, and infrastructure. Anaconda was recently recognized in the 2024 Gartner® Voice of the Customer for Data Science and Machine Learning Platforms report, finding that these platforms are “vital to manage the complexity of compiling data, models and infrastructure into scalable AI products, amid the surging demand for AI solutions like GenAI. ” The DSML Challenge Landscape Organizations today struggle with several critical challenges in the DSML space: Managing complexity across the entire AI development lifecycle Ensuring governance and security of open-source components Scaling deployments without sacrificing performance or reliability Balancing innovation with enterprise requirements As these challenges intensify, organizations need more than just tools—they need comprehensive solutions that provide security, flexibility, and performance throughout their AI journey. Anaconda’s Position in Peer Reviews In this context, we believe Anaconda has distinguished itself through exceptional customer satisfaction, as present in the Gartner Voice of the Customer report: 100% willingness to recommend — one of the three vendors to have the highest recommendation rate among all vendors evaluated 4 & 5 star rating distribution — with 50% of reviewers giving us 5 stars and 50% giving us 4 stars Deployment experience score of 4. 6 — we believe this strong rating highlights our focus on practical implementation Product capabilities rating of 4. 5 — for us, this excellent rating validates our technical foundations This validation from our users... --- - Published: 2025-04-22 - Modified: 2025-08-25 - URL: https://stage.anaconda.com/blog/illuminate-data-lumen-ai - Categories: Guides At Anaconda, we believe that accessible tools for data analysis empower individuals to discover insights that can lead to meaningful change. As Earth Day nears, considering how data and artificial intelligence can illuminate our understanding of critical infrastructure and environmental impact is top of mind. The democratization of data science through intuitive AI interfaces particularly creates new opportunities for environmental stewardship and informed decision-making. Lumen AI, our natural language interface for data exploration, demonstrates how conversational AI can transform raw information into visual narratives that inspire action. In this article, we’ll explore how Lumen can help us visualize and understand our energy landscape, bringing clarity to complex relationships between power generation facilities and data centers across the United States. The Power of Data in Environmental Understanding Environmental challenges are inherently data challenges. By bringing together diverse datasets and visualizing their relationships, we can uncover patterns that might otherwise remain hidden. For Earth Day, I decided to explore two critical infrastructure datasets: Power Plants: EPA data including location, fuel type, and emissions information Data Centers: Distribution of computing facilities across the country These datasets provide a window into the complex energy ecosystem that powers our digital world. By understanding where power is generated and consumed, we can identify opportunities for efficiency improvements and sustainability initiatives. Setting Up Our Exploration First, I needed to gather the necessary data. The EPA provides comprehensive information about power plants, while data center information is available through geographic service endpoints. With a few simple commands, I... --- - Published: 2025-04-17 - Modified: 2025-07-02 - URL: https://10.2.107.56:8443/blog/how-do-you-become-a-data-scientist - Categories: Perspectives There is not a single linear path for a career in data science. As a named discipline, data science didn’t exist until the last two decades. That means data scientists and those in related roles may have started their careers down a different path. So, how do you become a data scientist? Knowing a single right answer wasn’t possible, we decided to speak with five Anaconda employees to get their take. How did they build the skills necessary for their current roles? What would they recommend for someone hoping to enter the world of data science? Read on to learn more about their experience. Q: What Did You Study in School? Answers to this question varied. While there are common degrees, it is also possible to transition into data science without a STEM background. That being said, you do need to have an appreciation and a love for math and science. The most common undergraduate degrees:Computer ScienceMathematicsEngineeringData Science (becoming more common as universities offer programs)“I was technically oriented from a young age. I went into college for electrical engineering, focused on computer hardware and software. My ambition was to become a professor and an academic. Then, the internet boom happened when I was in grad school, and my plans changed. I discovered I was good at marrying software development and advanced mathematics together. There’s a lot of flavors to that, but it just so happened that my training meshed well with machine learning and artificial intelligence. ”– Michael Grant Vice... --- - Published: 2025-04-17 - Modified: 2025-08-07 - URL: https://stage.anaconda.com/top-ten-techniques-for-machine-learning-visualization - Categories: Technical Notes As part of any data science project, data visualization plays an important part in order to learn more about the available data and to identify any main pattern. Wouldn’t be great to also make as visually intuitive as possible the machine learning part of the analysis? In this article, we are going to explore some techniques that could help us to face this challenge, such as parallel coordinates plots, summary data tables, drawing ANNs graphs and many more. All the code used in this article is freely available on my Github and Kaggle accounts. TechniquesHyperparameter OptimizationHyperparameter optimization is one of the most common activities in machine/deep learning. Machine learning models tuning is a type of optimization problem. We have a set of hyperparameters (such as learning rate or number of hidden units) and we aim to find out the right combination of their values which can help us to find either the minimum (example: loss) or the maximum (example: accuracy) of a function. In one of my previous articles, I went into the details of how what kind of techniques we can use in this ambit and how to test them in a 3D space. In this article I will instead show you how we can accomplish that for reporting in a 2D space. One of the best solutions for this type of task is to use a parallel coordinates plot (Figure 1). Using this type of plot, we can in fact easily compare different variables (like features) together in... --- > This complete LLM project focuses on combining Mistral 7B and Llama2 for a high-performing AI chatbot on a local device. - Published: 2025-04-11 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/how-to-build-ai-chatbots-with-mistral-and-llama2 - Categories: Technical Notes In the ever-growing world of AI, local models have become a focal point, particularly for their advantages in privacy and safety. The capability to deploy and develop chatbots using local models is notably valuable for data security, privacy, and cost management. Mistral and Llama2 emerge as two of the best-performing open-source local large-language models (LLMs). Llama2, presented by Meta in July this year, is a state-of-the-art collection of LLMs. It offers 7-billion, 13-billion, and 70-billion parameter models, all of which are available free of charge for research and commercial use. Mistral 7B, released in September by Mistral AI, is recognized as the most powerful LLM for its size. It outperforms Llama2 13B on all benchmarks, even though it has fewer parameters and is thus faster and easier to work with. Leveraging these two foundational models, alongside a chat interface from the open-source project Panel, we will show you how easy it is to make AI chatbots with local models. In this post, you’ll learn how to: Use the Mistral 7B model Add stream completion Use the Panel chat interface to build an AI chatbot with Mistral 7B Build an AI chatbot with both Mistral 7B and Llama2 Build an AI chatbot with both Mistral 7B and Llama2 using LangChain Before we get started, you will need to install panel==1. 3, ctransformers, and langchain. Note that if you are using a Nvidia GPU, please install ctransformers. Now you are ready to go! Getting started with Mistral Let’s get started with a... --- > Learn how to set up a Windows virtual machine on a Mac so you can run Python in Excel to your heart’s content! - Published: 2025-04-11 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/how-to-run-python-in-excel-on-a-mac - Categories: Technical Notes If you use a Mac, you may have suffered FOMO on encountering the brouhaha surrounding the beta release of Python in Microsoft Excel — as for the time being, this feature is only available on Windows. In this blog post, I’ll show you how to set up a Windows virtual machine on a Mac so you can run ‘=PY’ to your heart’s content! Caveat: This is a less-than-ideal, short-term workaround if you’d rather not wait for native support for Python in Excel on a Mac. System Requirements To evaluate the pre-release version of Python in Excel, I used an M1 Apple Silicon MacBook Pro (2020) with 8 gigabytes of memory. With this quantity of system memory, I was able to run Excel on Windows just fine, though I noticed that my system would get a little sluggish if I had too many other memory-hungry applications open. If you’re on an Apple Silicon Mac, you have at least 8 gigabytes of memory, so you’ll have adequate resources to follow this tutorial. If you’re running an Intel Mac, the same basic steps should work, but your mileage may vary depending on the age and configuration of your system. Installing Parallels The first step to get Windows up and running is to install Parallels. You can go to parallels. com and click on the Free Trial link to test the software for up to 14 days before committing to a paid subscription. On the next page, click on the Download Trial button to... --- > This is the first article in a series of two. The second article is ChatGPT: AI-Assisted Writing Pros, Cons, and Tips to 3x Content Production . We have… - Published: 2025-04-04 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/chatgpt-is-adding-ai-writers-to-your-content-production-process-worth-it - Categories: Perspectives This is the first article in a series of two. The second article is ChatGPT: AI-Assisted Writing Pros, Cons, and Tips to 3x Content Production. We have entered the world of AI-assisted everything, and the latest mind-blowing artificial intelligence (AI) advances include ChatGPT. Within days of OpenAI’s release of the chatbot in November 2022, marketing leaders everywhere were asking their teams how many writers they could replace with this emerging and powerful technology. If you’re considering adding AI writers to your content production process, you’re not alone. Many enterprise teams are turning to ChatGPT as a way to speed up their content creation process and improve the quality of their output. However, AI writers aren’t ready to replace anyone on your content team. Let’s take a closer look at the highest-volume and best uses of ChatGPT and AI writers for enterprise content teams, based on my experience working with these tools for the last several months. What is ChatGPT? Generative models are one of the most significant advances in artificial intelligence (AI) today. OpenAI’s ChatGPT is the one that has gained the most attention over the last few months, and with good reason: built and trained on OpenAI’s group of GPT-3 large language models, ChatGPT (Chat Generative Pre-trained Transformer) has left most people open-jawed at its capabilities. Based on a written prompt you provide, it can generate text ranging from poetry and fiction to video demo scripts and blog articles. And, ChatGPT can write code. This screenshot from ChatGPT shows... --- - Published: 2025-04-04 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/anaconda-oss-growth-update-2025 - Categories: News Open source software powers the data science and AI revolution, providing the essential tools that researchers, developers, and organizations rely on daily. At Anaconda, we don't just use open source—we're actively building its future. In 2024 alone, our teams contributed thousands of commits across dozens of critical projects that millions of Python users depend on. As we look toward 2025, we're doubling down on our commitment with ambitious plans to make these tools faster, more accessible, and more powerful. This post offers a behind-the-scenes look at what we've accomplished and where we're headed next—showcasing how Anaconda's investment in open source is helping to shape the future of computational tools for everyone. Our Open Source Legacy and Vision for Democratizing AI Over the last decade, Anaconda has invested tens of millions of dollars in open source innovation through employee time, direct donations, event sponsorships, and more. This commitment has been integral to our growth and success, with some of our projects – like conda – dating back more than 12 years ago (conda's first commit was on Oct. 15, 2012). We believe that open source collaboration creates the strongest foundation for AI innovation, with our full-time engineers tackling complex, sustained work that strengthens the infrastructure millions of developers rely on daily. These contributions give us firsthand insights that drive improvements to projects including BeeWare, PyScript, conda, Jupyter, fsspec, Intake, Numba, SPy, Holoviz, Panel, and Lumen. The scale of our impact is remarkable: conda-forge now maintains over 27,000 packages serving as the... --- - Published: 2025-04-02 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/anaconda-code-brings-r-to-excel - Categories: News We're thrilled to announce the latest addition to Anaconda Toolbox: R language support in Anaconda Code! With this beta release, you can now harness the statistical power and visualization capabilities of R directly within your Excel worksheets, giving you even more flexibility in your data analysis toolkit. Let's explore a practical example that showcases how this integration can revolutionize your data workflows. With this powerful addition, Anaconda Code users can now script in both Python and R in the same Excel workbook, combining the two biggest data science languages with the world’s most popular productivity tool. Customizable Environment Anaconda Code means you can install R libraries directly into an R environment in your workbook, allowing you to choose from over 20k libraries to upgrade your statistical workflows. The environment is managed locally in the add-in, meaning your data stays in the workbook at all times.   Statistical Analysis Made Simple with R in Excel Consider a common challenge in data analysis: performing advanced statistical tests that go beyond Excel's built-in functions. While Excel offers basic statistical tools, R is renowned for its comprehensive statistical capabilities. Now, you can access these powerful tools without leaving your Excel environment. Anaconda Code’s REF function passes worksheet data directly into the R script where we can easily perform a one-way analysis of variance, printing the statistics to the cell’s log and returning a boxplot to the worksheet. Why R in Anaconda Code Is a Game-Changer Complementary Strengths: Combine R's statistical prowess with Excel's accessibility... --- > Anaconda Learning is moving to new Anaconda.com/learning domain, consolidating our platform into a single location and migrating to a new learning management system. Read the latest FAQs. - Published: 2025-03-28 - Modified: 2025-07-23 - URL: https://10.2.107.56:8443/blog/anaconda-domain-change-frequently-asked-questions - Categories: News In addition to announcing the formation of the GPU Open Analytics Initiative with H2O and MapD, today, we are pleased to announce an exciting collaborati... As you may know, Anaconda is moving the contents and services found on Anaconda. cloud to Anaconda. com, consolidating our platform into a single location. This change is also affecting Anaconda Learning. Here’s what you should know: What will change? Anaconda Learning is moving all content and services from its home on Anaconda. cloud to Anaconda. com/learning, consolidating our platform into a single location and migrating to a new learning management system, with improved navigation and design. Why are these changes happening? Our goal is to provide a more unified experience for our users. This change will make it easier to access all Anaconda Learning resources from a single domain while improving navigation and discoverability. What will be different about my experience? The core learning experience in all courses, learning paths, and certifications will remain unchanged. You'll still have access to the same high-quality content that you're used to. However, you may notice enhancements to the look and feel of the platform. These improvements are designed to create a more cohesive experience while maintaining all the functionality you rely on. If you are a free user, you will now be required to sign up for a free account and sign in to access learning content. This will also allow you to track your progress and earn completion certificates. When will these changes take effect? These changes will be rolled out by April 14, 2025. After this date, all traffic to Anaconda Learning on Anaconda. cloud will be automatically redirected to... --- > Microsoft Excel add-in brings AI-powered Anaconda Assistant, curated data catalogs, and cloud features to Python in Excel users. - Published: 2025-03-28 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/anaconda-toolbox-brings-ai-assistant-no-code-development-to-python-in-excel - Categories: Product New Microsoft Excel add-in brings AI-powered Anaconda Assistant, curated data catalogs, and cloud features to Python in Excel users. Today we’re announcing the release of Anaconda Toolbox, a new suite of tools built to enhance the experience and capabilities of Python in Excel. Toolbox will be accessible to current Python in Excel beta users through the Microsoft Marketplace. After the successful launch of Python in Excel last month, the new features added by Anaconda Toolbox will enable you to use Python in Excel, even if you don’t know Python. Included in Toolbox is Anaconda Assistant, the recently released AI assistant designed specifically for Python users and data scientists, which can guide you in your first steps or supercharge your work, even if you have advanced experience. Python in Excel beta users can sign up to experience Anaconda Toolbox today. What’s in the Anaconda Toolbox for Python in Excel? Anaconda Toolbox enables anyone, regardless of experience, to quickly generate code and visualizations while learning Python along the way. Because the code runs in Excel, you know how it will work when you share the file with others, even if they don’t have Toolbox. Here is what’s included in Toolbox:Anaconda Assistant for Python in ExcelKnow what you want to do, but don’t know how to do it in Python? Just ask Anaconda Assistant. When it gives you the code, just push it to the Excel grid, where you can edit and run it just like other Python code. If you start with... --- > You can use Anaconda Assistant, our AI-powered chat interface, while coding on your local machine. Learn more in this article. - Published: 2025-03-28 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anacondas-ai-assistant-comes-to-the-desktop - Categories: Product You now can take advantage of our AI chat assistant while coding on your local machine, with our new Anaconda Toolbox! Though many users have moved to the cloud, we still see a massive number of local downloads of the Anaconda distribution, so we aim to bring the same experience we’re building in our Anaconda Notebooks cloud offering to the desktop. Now, we’re kicking it off with the Anaconda Assistant! With the Anaconda Assistant, you can:Quickly get started with AI-driven coding examplesEasily write code or create visualizations based on simple requests with our chatbotIdentify and debug Python errorsDocument and clean up your code... All while coding on your own device! Try it—free! Regular Anaconda Assistant local use requires a paid Anaconda Cloud subscription, but free account users get 30 trial Assistant requests to try it out. If you’re still not sure about a paid subscription after that, you can continue to use the Assistant for free on the Anaconda Cloud Notebooks service. Here’s how Assistant pricing works across all of our cloud tiers:TierFreeStarterPro / BusinessCloud Notebooks30 requests per day60 requests per day120 + larger context windowLocal Notebooks30 requests totalSame as CloudSame as CloudAs always, these limits may change in the future. We’ll be watching usage and adjusting limits to make sure we’re providing a great experience across all tiers. Bringing the Cloud Experience HomeBack when we launched Anaconda Notebooks, our goal was (and still is) to provide a standard and familiar Jupyter experience coupled with helpful tools and add-ons to... --- > In this blog post, we will explore how the new Python in Excel feature enables a completely new way to work with time series data in Excel. - Published: 2025-03-28 - Modified: 2025-06-10 - URL: https://10.2.107.56:8443/blog/analyzing-time-series-data-with-python-in-excel - Categories: Technical Notes In this blog post, we will explore how the new Python in Excel feature enables a completely new way to work with time series data in Excel. Thanks to its built-in integration with Anaconda Distribution, it is now possible to leverage on Python data modeling capabilities, making time series analysis in Excel a whole new experience. Note: To reproduce the examples in this post, install the Python in Excel trial. A Time Series Primer If you want to predict trends in temporal data, time is an important factor that must be considered in many types of analyses. Classic examples of this kind of analysis can be found in many domains. For example, in finance time series analysis can be used by stock trades to get a better understanding of various stock prices. Similarly in healthcare, temporal data (generally also referred to as longitudinal data) are used to denote the health trajectories of patients, with the intention to predict disease or recovery progression. Another example of time series analysis can be found in meteorology, where temporal data are used in many use cases like temperature forecasting or air quality control. In this blog post, we will consider an example of air pollution forecasting, exploring how the new integration of Python would allow for unprecedented time series analysis features in Excel. In simple terms, a time series can be defined as a series of data points, ordered in time. Most commonly, a time series comprises a sequence of successive equally spaced points... --- - Published: 2025-03-28 - Modified: 2025-07-08 - URL: https://10.2.107.56:8443/blog/anaconda-ai-gartner-report - Categories: News AI isn’t just transforming industries—it’s reshaping the global competitive landscape and introducing risks that demand immediate action. As enterprises scale AI efforts to unlock productivity and innovation, they must navigate emerging challenges around governance, security, and disinformation. The latest strategic report from Gartner®, Top Strategic Technology Trends for 2025, highlights three key AI imperatives and risks for organizations looking to thrive in this new era: agentic AI, AI governance platforms, and disinformation security. Here’s a breakdown of what Anaconda believes is coming and how organizations can respond with confidence. Agentic AI: The Next Evolution in ProductivityThe era of agentic AI has arrived. Autonomous systems that act on behalf of users—executing multi-step tasks, analyzing data, and generating tailored outputs—are redefining how work gets done. This shift isn’t theoretical. Early-adopters are already seeing the competitive advantage of agentic systems, as AI assistants automate coding, reporting, and decision-making processes. According to Gartner, “by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024”. Recent events highlight how quickly agentic AI is accelerating. Just 24 hours after OpenAI launched its proprietary “Deep Research” feature, HuggingFace responded with its open-source alternative, underscoring how rapidly open innovation can rival proprietary models. Meanwhile, AI-native startups are embracing agentic AI as the default, bypassing the slower iterative processes of legacy enterprises. These startups prioritize agility, embedding lightweight models that enable continuous, automated workflows. As this trend gains momentum, enterprises that hesitate risk falling behind. The takeaway? Agentic AI is the new... --- > Conda is a popular package and environment management system that offers a range of benefits compared to other solutions. Here’s why you might choose conda over other options. - Published: 2025-03-24 - Modified: 2025-06-17 - URL: https://10.2.107.56:8443/blog/12-reasons-to-choose-conda - Categories: Perspectives If you’re diving into the world of data science, machine learning, or scientific computing, managing dependencies and creating isolated environments is essential. This is where conda shines. Conda is a popular package and environment management system that offers a range of benefits compared to other solutions like pip, virtualenv, and system-level package managers. Here are 12 reasons why you might choose conda over other options. Governance: Conda is a community-supported open-source project, governed by a multi-stakeholder organization and fiscally sponsored by the NumFOCUS non-profit organization. With the Conda Steering Council as its governing body, conda is catering to its user and community growth. Together with other contributing members of the conda organization, Anaconda helps ensure conda is sustainably maintained over the long-term. Extensibility: Conda is extensible with a growing plugin API, making it possible for users and developers to extend it with additional tools and workflows. This is possible due to its stable, dependable code base, which supports the majority of use cases. Cross-platform compatibility: Conda is platform-agnostic, making it a perfect choice for multi-platform development. It works seamlessly across Windows, macOS, and Linux, ensuring consistent package management regardless of the operating system. Package management: Conda’s package management system is robust and versatile. It supports not only Python packages but also packages written in other languages, such as R and C++. This makes it an excellent tool for projects that require a mix of languages or depend on non-Python libraries. Binary distribution: Conda offers pre-compiled binary packages, which means you... --- > We have updated the Anaconda Learning library so you can search and access the most recent and best courses using Anaconda Notebooks. - Published: 2025-03-21 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-learning-turbocharge-your-python-journey-in-anaconda-notebooks - Categories: News In addition to announcing the formation of the GPU Open Analytics Initiative with H2O and MapD, today, we are pleased to announce an exciting collaborati... If you’ve been considering learning Python or are interested in creating projects with AI, then now is the perfect time to start. We have just updated the Anaconda Learning library, which enables you to easily search and access the most recent and best courses using Anaconda Notebooks. With a hands-on tool available at your fingertips, you can speed up your learning process and expand your knowledge of topics ranging from data wrangling to the fundamentals of large language models (LLMs). All Anaconda Cloud users now have access to the notebook content of every Anaconda Learning course. Start learning Python today within Anaconda Notebooks Whether you’re just starting out with Python and want to learn the fundamental concepts of strings, dictionaries, and dataframes or are an experienced practitioner who wants to expand your knowledge to machine learning, the Anaconda Learning app has the notebooks you need to get started. Use the handy search bar to find the relevant courses and explore the different topics available. Learn and code directly within the comfort of Jupyter Notebook A common pain point of online learning is the requirement to use an online portal to run and execute the code within the confines of an unfamiliar tool. With Anaconda Learning, it’s all run directly from the commonly used Jupyter Notebook, with the flexibility to customize the code and run additional tests and experiments in line with the course content. With the ability to hop in and out of a course, you can load the specific... --- - Published: 2025-03-18 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/anaconda-nvidia-enable-seamless-gpu-integration-for-jupyter-notebooks - Categories: News The strategic partnership empowers enterprises to create secure and scalable generative AI applications with accelerated computing capabilities and simplified workflows. Much like the move from assembly to high-level languages revolutionized software development in the past, AI is now driving a similar transformation. While artificial intelligence is fundamentally changing how we build technology, Python maintains its position as the dominant programming language for AI development. To help enterprises and developers evolve their AI capabilities safely and responsibly, Anaconda is today expanding its partnership with NVIDIA to: Further accelerate data and AI processing by distributing CUDA libraries as part of the Anaconda Platform for enterprises Improve accessibility to GPUs in Jupyter Notebooks—available first as private preview in Anaconda Notebooks Building on Momentum Last year, Anaconda took a massive step toward simplifying how developers work with AI technology by integrating NVIDIA’s CUDA Toolkit 12 into our platform. Since then, thousands have benefited from this partnership to focus on innovation rather than wrestling with technical setup. “The impact of these tools is already evident,” says Thomas Nield, Engineering Lead at Yawman Flight. “The integration of NVIDIA GPUs with Anaconda’s platform has transformed how I manage deep learning workflows. Being able to selectively accelerate computation-heavy tasks while keeping our development environment isolated allows faster and more focused R&D instead of managing infrastructure. ” Introducing GPU- Powered Jupyter Notebooks This week, at NVIDIA GTC 2025, we’re launching in private preview, a native, easier way to enable GPU access through Jupyter Notebooks to make AI development more... --- - Published: 2025-03-13 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-ai-predictions-2025 - Categories: Perspectives Anaconda recently brought together some of the brightest minds in AI for our 2025 AI Predictions Webinar. The panel featured: Peter Wang, co-founder and CAIO of Anaconda Greg Jennings, Head of Engineering – AI at Anaconda Priyanka Kulkarni, founder and CEO of Casium David Pitman, Engineering Leader in AI and Startup and VC Advisor Together, they explored the trends, innovations, and challenges shaping the future of AI. Let’s dive into some key predictions, takeaways, and insights they shared during the event. Prediction 1: The Rise of Small Models and On-Device AI A standout theme of the webinar was the growing momentum behind smaller AI models. These models are set to revolutionize AI applications by enabling on-device processing, which reduces reliance on cloud infrastructure while addressing critical privacy concerns. Panelists noted that larger models might have more features than necessary for most projects, while smaller models could better support more specialized purposes. “Maybe in 2025, the small models will get so good that we’ll call the larger ones overbuilt models,” said Peter Wang, CAIO of Anaconda. “We’re seeing strong performance coming out of those models, and people are getting better at knowing how to use LLMs. We’re going to see a lot more examples of big models using small models to do very narrow things. ” The panelists emphasized that for task-specific items, starting with the problem and deciding what the model will do to solve it often has better results than relying on a larger model that knows everything on... --- > IBM and Anaconda have collaborated to provide an enterprise-grade generative AI solution that is natively built within IBM watsonx.ai. - Published: 2025-03-11 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/address-the-need-for-python-in-generative-ai-with-ibm-watsonx-ai-and-anaconda - Categories: News For the last decade, Anaconda has helped define the guard rails and infrastructure of Python programming to empower a new generation of data science prof... The advent of large language models (LLMs) can democratize access to AI and advance enterprise adoption of AI technology. In this time of discovery and experimentation, the governed use of AI is more critical than ever. That’s why IBM and Anaconda are collaborating to provide an enterprise-grade generative AI solution. Natively built within IBM watsonx. ai, Anaconda allows data scientists to use Python and open source to unleash innovation with AI. IBM watsonx. ai users can access Anaconda’s natively built open-source Python repository. Anaconda Distribution provides users with Python open-source package management. Watsonx. ai can also integrate with Anaconda Repository on-premises for Python security vulnerability management and license management. Python continues to be the leading language for data science and generative AI workloads in 2024. Anaconda is the gateway to the open-source Python community, providing curated access to the packages powering enterprise AI. Through this collaboration, watsonx. ai brings Generative AI models to clients, and Anaconda brings enterprise-grade Python to enhance enterprise AI. Python and LLMs in 2024: How data scientists can leverage watsonx. ai and Anaconda To help advance enterprise AI initiatives, Anaconda Distribution has been updated to include key Generative AI packages like huggingface_hub, transformers, and safetensors. These packages leverage open-source development to expedite time to value for deploying large language models (LLMs) and other models in the enterprise. Anaconda’s clear mission and dedication to innovation have led to its core open-source package management system being used in AI frameworks at Meta, NVIDIA, PyTorch, OpenAI, and now IBM... --- - Published: 2025-03-10 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/beeware-mobile-python - Categories: News Twenty years ago, “computing” meant a desktop box or laptop. Today, your smartphone is likely the most powerful computer you carry—always with you, packed with sensors, cameras, and GPS capabilities that traditional machines can’t match. Yet despite this shift, Python developers have been largely locked out of this crucial platform. If a phone is just a computer, shouldn’t we be able to use Python to program it? Until recently, the answer was “not easily. ” Python on mobile devices has existed informally for years, but without mainstream adoption. The ecosystem of packages that make Python valuable—Numpy, Pandas, Matplotlib and more—simply didn’t have iOS or Android compatible versions, and lacked mechanisms to share that support. The open source software (OSS) engineering team at Anaconda has been working to change this. For the past three years, Anaconda has supported the BeeWare Project to break down these barriers, coordinating efforts across the Python ecosystem with significant breakthroughs in recent months that will reshape how developers build applications. Three challenges to bringing Python to mobile Adding mobile platform support to Python itself The first problem is making Python work on mobile devices at all. Mobile devices run operating systems that are highly specialized, requiring unique hardware requirements and strict security environments to work properly. Even compiling code requires a specialized approach. Unlike desktop systems where you compile on the same OS that will run the code, you must compile for iOS or Android on a desktop computer and ship the result to the device.... --- - Published: 2025-03-07 - Modified: 2025-07-06 - URL: https://10.2.107.56:8443/blog/persona-programming-ai - Categories: Technical Notes There are no solved problems; there are only problems that are not yet solved. ” – John CarmackAt Anaconda, we’re constantly exploring how to help developers get the most from AI tools. When generating code with Large Language Models (LLMs), the quality of output can vary dramatically based on subtle prompt differences. But what if there’s a more systematic approach to optimizing these interactions? Intrigued by recent reports that found LLMs capable of reasoning and overperforming when assigned specific roles, we set out to answer a simple yet powerful question: Can we influence not just what code LLMs generate, but how they generate it, by invoking the mindsets of legendary programmers? Using Anaconda’s Assistant-enabled notebooks, we set out to explore whether the latent space of LLMs contains distinct “programming personalities” that could be channeled for specific coding tasks. The results weren’t just surprising—they fundamentally challenge how we might approach AI-assisted development going forward. The Seeds of Inspiration: Woolf and CarmackMy journey began with two distinct but equally compelling sources of inspiration. First, Max Woolf’s insightful blog post, “Can LLMs write better code if you keep asking them to “write better code”? ” caught my attention. Woolf’s experiment elegantly demonstrated that even a vague, iterative prompt like “make it better” could coax an LLM into producing progressively more optimized code. He started with a basic Python solution to a numerical problem and, through repeated prompting, ended up with a highly optimized, multi-threaded, and even “enterprise-ready” implementation, complete with logging and graceful... --- - Published: 2025-02-19 - Modified: 2025-07-06 - URL: https://10.2.107.56:8443/blog/anaconda-launches-lumen-ai - Categories: News AI is transforming data science, but for many teams, the challenge isn’t just having access to AI—it’s making it work in real-world workflows. That’s why we at Anaconda are thrilled to introduce Lumen AI, a new open-source AI tool designed to help teams explore, transform, and visualize data using natural language. Whether you’re a data scientist looking to streamline your workflow or a business leader trying to make sense of complex data, Lumen makes AI-powered analytics more intuitive and accessible. Why Lumen AI? AI-driven, agent-based systems are rapidly changing how businesses operate, but many organizations still struggle with technical barriers, fragmented tools, and slow, manual processes. Lumen eliminates those roadblocks by giving users an AI-powered environment to quickly generate SQL queries, analyze datasets, and build interactive dashboards—all without writing code. “As AI costs decline and open-source models rival proprietary alternatives, now is the time for businesses to embrace truly transformative AI solutions,” said Peter Wang, co-founder and Chief AI and Innovation Officer at Anaconda. “Lumen turns the potential of untapped data into action, helping teams accelerate innovation, optimize workflows, and uncover new opportunities—without needing deep technical expertise. ”What Can You Do With Lumen? Lumen is designed to make advanced data workflows more intuitive and scalable for teams of all sizes. Some of its key capabilities include: Automated Data Pipelines – Generate SQL queries and transform data seamlessly across local files, databases, and cloud data lakes. Interactive Visualization – Instantly create charts, tables, and dashboards using natural language—no coding required. Custom... --- > This post refers to Anaconda Enterprise 4. To generate custom parcels in Anaconda Enterprise 5, see here . Earlier this year, as part of our partnership… - Published: 2025-02-14 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/self-service-open-data-science-custom-anaconda-parcels-for-cloudera-cdh - Categories: Technical Notes This post refers to Anaconda Enterprise 4. To generate custom parcels in Anaconda Enterprise 5, see here.Earlier this year, as part of our partnership wi... This post refers to Anaconda Enterprise 4. To generate custom parcels in Anaconda Enterprise 5, see here. Earlier this year, as part of our partnership with Cloudera, we announced a freely available Anaconda parcel for Cloudera CDH based on Python 2. 7 and the Anaconda Distribution. The Anaconda parcel has been very well received by both Anaconda and Cloudera users by making it easier for data scientists and analysts to use libraries from Anaconda that they know and love with Hadoop and Spark along with Cloudera CDH. We’re excited to announce a new self-service feature of the Anaconda platform that can be used to generate custom Anaconda parcels and installers. This functionality is now available in the Anaconda platform as part of the Anaconda Scale and Anaconda Repository platform components. Earlier this year, as part of our partnership with Cloudera, we announced a freely available Anaconda parcel for Cloudera CDH based on Python 2. 7 and the Anaconda Distribution. The Anaconda parcel has been very well received by both Anaconda and Cloudera users by making it easier for data scientists and analysts to use libraries from Anaconda that they know and love with Hadoop and Spark along with Cloudera CDH. Since then, we’ve had significant interest from Anaconda Enterprise users asking how they can create and use custom Anaconda parcels with Cloudera CDH. Our users want to deploy Anaconda with different versions of Python and custom conda packages that are not included in the freely available Anaconda parcel. Using parcels... --- > Discover 5 lesser-known sites for finding open datasets: Google Dataset Search, OpenML, FiveThirtyEight, and more. Perfect for ML and data science projects. - Published: 2025-02-14 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/useful-sites-for-finding-datasets - Categories: Perspectives Open data fuels innovation. It enables people to focus more on research than on data collection, which is both time-consuming and expensive. In this series of articles, Getting Datasets for Data Analysis tasks, we are looking at ways to access datasets from the internet. In the first part, we learned to streamline Google search to find only specific files on the web. In this part, let’s look at some of the sites, which host free and openly available datasets that can be used for data analysis tasks. Some of the sources are pretty well known in the data science community like the UCI Machine Learning Repository, Kaggle datasets, and Data. gov, so I won’t touch upon them in this article. Instead, let’s focus on some lesser-known dataset aggregator sites. Is the Data FAIR? Making data publicly available is vital for the benefit of the research community and society as a whole. However, the shared data should follow some essential guidelines so that it can be put to maximum use. In “The FAIR Guiding Principles for Scientific Data Management and Stewardship,” Wilkinson et al. , lay down the principles for data management and data sharing. FAIR is an acronym that stands for data that isFindableAccessibleInteroperableReusableFAIR Data principles as laid down by Wilkinson et alLet’s now look at some of the useful sites for finding open and publicly available datasets, quickly and without much hassle. 1. Google Dataset SearchScreenshot of the Google Dataset Search page (Image by Author)Google Dataset Search is a... --- - Published: 2025-02-12 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/the-power-of-local-data-science-and-ai-with-anaconda-and-lenovo-workstations - Categories: Technical Notes Why Work Locally? In a world of ever-growing AI models with hundreds of billions of parameters, a lot of industry focus is placed on the extremes of computing. We read about companies working with petabytes of raw data, trillions of filtered data points, and the tools they use to train on clusters that cost $500K a day to rent. While these efforts represent an exciting niche of AI and ML, there’s a tremendous number of data tasks that do not require the cost or complexity of mega-scale AI projects. How should a data scientist tackle much more common problems where the scale might be “only” 10-100 GB of data? The answer might be simpler than you think! We’ve learned a lot about how customers do their work over the years, and we’ve found that many data scientists begin their projects on their own laptops. They stage the data they are analyzing on the laptop’s internal storage and process it using Python scripts or Jupyter Notebooks running on the laptop itself. But why work this way, when the cloud is so ubiquitous and powerful? The biggest reason is avoiding friction. No matter how easy, provisioning a cloud server requires some effort (and sometimes approval), and getting data and software packages to and from that server is an additional chore and cost. A data scientist’s own computing device is always provisioned for their use, preloaded with the tools they prefer, and has few incremental costs to justify. Laptop users can freely switch... --- - Published: 2025-02-12 - Modified: 2025-07-11 - URL: https://10.2.107.56:8443/blog/anaconda-for-education-empowering-the-academic-community - Categories: News At Anaconda, we believe in empowering educators, students, and researchers to achieve their full potential in data science and machine learning. Today, we’re excited to announce Anaconda for Education, a new initiative offering free access to our premium features for verified academic users. Why Anaconda for Education? We understand the unique challenges faced by the academic community—whether you’re an instructor simplifying software setup for your classroom, a student eager to learn cutting-edge tools, or a researcher managing complex data analyses. With Anaconda for Education, academic users gain complimentary access to: Cloud Notebooks with 10GB of storage and daily compute time. AI-Powered Tools, including Anaconda Assistant and AI Navigator, to enhance productivity and streamline workflows. Comprehensive Learning Resources, such as our full on-demand course catalog, monthly live training, and an Anaconda Certified Conda Fundamentals course (with more certifications coming soon). A consistent, open-source platform designed to reduce barriers and enhance learning opportunities. Who’s Eligible? If you’re actively enrolled or employed at an accredited academic institution and have a verified academic email address, you’re eligible to join Anaconda for Education with a free account.  Our goal is to support your journey, whether it’s for coursework, research projects, or self-directed learning. Driving Innovation in Academia This initiative aligns with our mission to democratize data science and foster a thriving ecosystem of learners and practitioners. By offering these resources for free, we aim to reduce financial barriers and encourage widespread adoption of data science tools in academic settings. How to Get Started Getting access is easy. Simply sign up for a... --- - Published: 2025-02-11 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/copilot-in-excel-with-python - Categories: Technical Notes For business professionals and analysts, Excel has long been the go-to tool for handling data. Yet, its limitations often require users to juggle external platforms for more advanced tasks like automating workflows, building custom functions, or conducting deep data analyses. Enter Copilot in Excel with Python: a game-changing combination that transforms how you work with data by bringing the power of Python and AI-driven assistance directly into your spreadsheets. In this post, we’ll dive into the synergy between Excel and Copilot, showing how this duo can supercharge your workflow. From automating repetitive tasks with Python scripts to using Copilot’s AI capabilities to generate complex formulas or debug your code, you’ll see how these tools work together to bridge gaps and elevate your productivity. Whether you’re optimizing financial models, analyzing datasets, or building interactive dashboards, Copilot in Excel with Python unlock new possibilities for data handling and decision-making. Getting started is simple: Copilot suggests code and formulas as you type, while Python’s seamless integration with Excel lets you run powerful libraries like Pandas, NumPy, and Matplotlib within your workbook. Together, they empower users of any skill level to streamline tasks, uncover insights, and make data-driven decisions—faster and more effectively than ever before. Getting Set Up Getting started with Copilot in Excel is like adding a powerful co-pilot to your workflow, always ready to assist and elevate your spreadsheet game. Whether you’re optimizing models, automating tasks, or performing advanced analyses, setting up Copilot in Excel with Python is straightforward. To begin, open... --- - Published: 2025-02-11 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/panel-dashboard-with-snowpark - Categories: Technical Notes Data scientists often use SQL to interact with a data warehouse, but then often rely on Python for data discovery, visualization, and modeling. How great would it be if we can interact with a data warehouse directly with our preferred tools in Python? Snowflake now natively supports Python with Snowpark for Python. It enables us data scientists to code in Python while enjoying the same security, performance, governance, and manageability benefits Snowflake has to offer. With this tool, I can interact with my data warehouse, visualize data, and even build and deploy models back to my data warehouse directly all in Python. To understand what is in the database, one of the first steps is to visualize your data. In this article, I will show you how I create this Panel dashboard to meaningfully visualize the 5 million data points from a Snowflake dataset. What is Snowpark for Python? Snowpark for Python allows data scientists to write our familiar Python code and translate Python back to SQL in Snowflake. With its partnership with Anaconda, we can use all the secure and well-curated Python packages for Snowpark. Snowflake even has its own Python package repository in Anaconda. Snowpark for Python is still in preview. I’m not exactly sure when this will become public. But you can request access via this link. What is Panel? Panel builds interactive dashboards and apps. It’s like R Shiny, but more powerful. It was developed by my Anaconda colleagues Philipp Rudiger, Jean-Luc Stevens, and Jim Bednar.... --- - Published: 2025-02-07 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/miniconda-25-1-1-release - Categories: News We are excited to announce the 25. 1. 1 release of Miniconda, Anaconda’s miniature version of the Anaconda Distribution installer. It includes:Python – the most widely used programming language for artificial intelligence, data science and machine learningconda – the open-source, cross-platform package and environment managerMiniconda is free to download, easy to install, and comes with free community support. The installer is pre-configured to access Anaconda’s public package repositories that include over 33,000 open-source AI, data science, and machine learning packages across seven different platforms. Download Miniconda 25. 1. 1 today and read the release notes for the full list of user-facing changes and packages in this release. Please note: The Miniconda installer is subject to Anaconda’s Terms of Service. Conda 25. 1. 1Miniconda 25. 1. 1 ships with conda 25. 1. 1. The full release notes describe all the user-facing enhancements and bug fixes captured in this release. Stay CurrentDownload Miniconda 25. 1. 1 today. We thank you for continuing to be a part of the Anaconda community. Please keep an eye on this blog to stay current on releases, including the next release of Miniconda, targeted for April. --- - Published: 2025-01-29 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/how-to-build-your-own-panel-ai-chatbots - Categories: Technical Notes With its latest version 1. 3, the open-source project Panel has just introduced an exciting and highly anticipated new feature: the Chat Interface widget. This new capability has opened up a world of possibilities, making the creation of AI chatbots more accessible and user-friendly than ever before. In this post, you’ll learn how to use the ChatInterface widget and to build:A basic chatbotAn OpenAI ChatGPT-powered AI chatbot A LangChain-powered AI chatbot Before we get started, you will need to install Panel (any version greater than 1. 3. 0) and other packages you might need like jupyterlab, openai, and langchain. Now you are ready to go! Use the ChatInterface widgetThe brand new ChatInterface widget is a high-level widget, providing a user-friendly chat interface to send messages with four build-in operations:Send: Send messages to the chat logRerun: Resend the most recent user message Undo: Remove the most recent messagesClear: Clear all chat messages Curious to know more about how `ChatInterface` works under the hood? It’s a high-level widget that wraps around the middle-level widget `ChatFeed` that manages a list of `ChatMessage` items for displaying chat messages. Check out the docs on ChatInterface, ChatFeed and ChatMessage to learn more. 1. Build a basic chatbotWith `pn. chat. ChatInterface`, we can send messages to the chat interface, but how should the system reply? We can define a `callback` function! In this example, our `callback` function simply echoes back a user message. See how it’s becoming more functional already? How to use the ChatInterface widget to... --- - Published: 2025-01-29 - Modified: 2025-06-27 - URL: https://10.2.107.56:8443/blog/how-to-build-a-retrieval-augmented-generation-chatbot - Categories: Technical Notes Retrieval-augmented generation (RAG) has been empowering conversational AI by allowing models to access and leverage external knowledge bases. In this post, we delve into how to build a RAG chatbot with LangChain and Panel. You will learn:What is retrieval-augmented generation (RAG)? How to develop a retrieval-augmented generation (RAG) application in LangChainHow to use Panel’s chat interface for our RAG applicationBy the end of this blog, you will be able to build a RAG chatbot like this: What is Retrieval-Augmented Generation (RAG)? Are you interested in making a chatbot that can make use of your own collections of data when answering questions? Retrieval-augmented generation (RAG) is an AI framework that combines the strengths of pre-trained language models and information retrieval systems to generate responses in a conversational AI system or to create content by leveraging external knowledge. It integrates the retrieval of relevant information from a knowledge source and the generation of responses based on that retrieved information. In a typical RAG setup:Retrieval: Given a user query or prompt, the system searches through a knowledge source (a vector store with text embeddings) to find relevant documents or text snippets. The retrieval component typically employs some form of similarity or relevance scoring to determine which portions of the knowledge source are most pertinent to the input query. Generation: The retrieved documents or snippets are then provided to a large language model, which uses them as additional context for generating a more detailed, factual, and relevant response. RAG can be particularly useful when... --- - Published: 2025-01-28 - Modified: 2025-07-22 - URL: https://10.2.107.56:8443/blog/learn-panel-python-in-2025 - Categories: Technical Notes Training for the Boston Marathon requires consistency, commitment, and a way to track progress over time. Imagine having a custom-built habit tracker that not only keeps you on pace but also helps visualize your progress—all powered by Python! Today, we’ll walk through how to build a complete habit-tracking web app using Python, from start to finish. No need for JavaScript—this is a full-stack Python project designed to help you stay on track with your marathon milestones. We’ll be leveraging the recently released panel-full-calendar, which is an extension of Panel. (You can wrap any Javascript libraries into Python with your own Panel extensions with: copier-template-panel-extension! )Let’s get started with two straightforward lines:from panel_full_calendar import Calendar Calendar. showWe can see a Calendar launch in our browser: It’s a little crowded, so let’s set the sizing_mode to stretch to the whole screen:from panel_full_calendar import Calendar Calendar(sizing_mode="stretch_both"). show Much better! In the upcoming app, we won’t need to view the week or day, so let’s also update the header_toolbar. Since we will need more imports later, let’s just do that now too. import sqlite3 import datetime import panel as pn import pandas as pd from panel_full_calendar import Calendar, CalendarEvent calendar = Calendar( sizing_mode="stretch_both", header_toolbar={ "left": "today", "center": "title", "right": "prev,next", }, ) calendar. show A calendar without events isn’t so useful. So let’s add some! First, “Happy New Year! ” You can pass date strings directly into add_eventnew_years_str = "2025-01-01" calendar. add_event(new_years_str, title=" Happy New Year! ")Alternatively, you can also pass in a datetime... --- - Published: 2025-01-23 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/package-download-data-updates-fixes-for-accurate-statistics - Categories: Perspectives TL;DR: We've rolled out major improvements to our package download statistics, including more accurate download counts and better tracking of . conda artifacts. If you maintain or use conda packages, you now have access to more reliable data about how these packages are being used across the ecosystem. A Window into the Conda Ecosystem Since 2017, Anaconda Package Data has been our community's window into how conda packages are being used. This dataset tracks download statistics from both Anaconda Distribution channels (repo. anaconda. com) and the anaconda. org public repository, helping maintainers and users understand the reach of their packages. What's New? When we first launched this public dataset in 2019, our goal was to provide transparency into package usage. Today's updates make that data significantly more accurate and comprehensive. More Accurate Download Counts We've rebuilt our data pipeline from the ground up. By directly processing raw HTTP request data from both anaconda. com and anaconda. org, we now capture a more complete picture of package downloads, including . conda artifacts that we previously missed. We also discovered and fixed an important issue: download counts for March-May 2024 were accidentally inflated due to CDN clone requests. By properly filtering requests to conda-static. anaconda. org, we've eliminated this double-counting. Channel Coverage Packages on anaconda. org are organized in channels and can be maintained by the community or a company. The anaconda-package-data dataset covers a wide range of popular conda channels, including: anaconda bioconda conda-forge nvidia plotly pytorch and pytorch-test pyviz rapidsai... --- - Published: 2025-01-22 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/python-in-excel-for-supply-chain-and-manufacturing - Categories: Perspectives For years, Excel has been the essential tool for operations managers and analysts, providing a powerful platform for managing data, creating reports, and handling day-to-day tasks. However, when it comes to advanced forecasting or optimization, Excel alone can sometimes fall short. Operations teams often need to rely on additional tools, such as Python or R, to perform complex analysis and modeling. Now, with Python integrated directly into Excel, these advanced capabilities are just a formula away. This combination brings Python’s powerful data-processing and analytical tools into the familiar Excel environment, creating a seamless workflow for inventory forecasting, route optimization, and beyond. No more switching between software, exporting data back and forth, or using numerous add-ins. Python unlocks new potential within Excel, making advanced techniques accessible right in your existing spreadsheets. In this post, we’ll dive into real-world examples that show how Python in Excel can transform inventory and supply chain management. From demand forecasting to route optimization, we’ll walk through practical ways to leverage Python’s time-series modeling and algorithmic capabilities—all within Excel. With Python’s robust libraries integrated into your spreadsheet, you can gain deeper insights, make data-driven decisions, and streamline operations. To start using Python in Excel, simply type “=py(“. This opens an embedded Python editor in Excel, where you can apply Python functions, use libraries like Pandas and Matplotlib, and analyze data—all without leaving your spreadsheet. Whether you’re visualizing demand trends or plotting optimized delivery routes, Python in Excel makes advanced data analysis more accessible than ever. Example: Demand... --- - Published: 2025-01-21 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/state-of-data-science-2024-key-findings - Categories: Perspectives For the seventh consecutive year, we conducted our State of Data Science survey to surface insights about the demographics of the data science community, use cases across industries, and trends related to artificial intelligence (AI) and open source. We’ve seen an increase in the usage of AI, with 87% of data scientist practitioners spending as much or more time on AI techniques this year versus last year. These AI techniques include generative adversarial networks (GANs), deep learning, and transformer models. Humans are also less concerned with AI overtaking their work—only about one in five respondents (22%) are fearful that AI might take their jobs, a steep decline from last year. Instead, they’re learning how to use AI to complement their skill sets and free up time from mundane, time-consuming activities. “A workforce in tune with company data and systems can better integrate AI solutions into existing processes,” says Barry Libert, Vice Chairman and CEO, Anaconda. Start by identifying essential AI skills that align with your business objectives, develop tailored learning pathways, build collaborative learning environments, partner with educational institutions for further development, and measure and iterate. AI techniques are fundamentally changing how businesses operate. The ones who take the right approach will thrive. ” There are also several encouraging updates among respondents, with many finding open source to be particularly helpful for their workflows and for collaborating. When asked to rank the top value of open-source software, respondents’ top answers were “most economical option,” “speed of innovation,” or “most useful... --- - Published: 2025-01-09 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/level-up-your-snowflake-notebook-with-enterprise-python-analytics - Categories: News Snowflake Notebooks, launched in summer 2024, provides a convenient and easy-to-use environment for Python, SQL, and Markdown using a cell-based development interface integrated within Snowflake’s secure, scalable platform. Anaconda’s secure, efficient, and robust Python packages are now available directly within Snowflake Notebooks, accelerating data science, machine learning, and AI development initiatives. Python's extensive ecosystem helps you address complex enterprise use cases with ease. Whether you're an SQL expert looking to expand your toolkit or an engineer seeking enterprise-grade solutions, understanding how to leverage Python effectively within Snowflake Notebooks is crucial for modern analytics. Why Bridge SQL and Python? Python is a critical tool in your analytic toolkit when analyzing data in the enterprise. SQL alone is simply not going to cut it. While SQL excels at data manipulation, it lacks Python's capacity for statistical analysis, API integrations, and data visualization. Having access to a polyglot notebook lets data scientists do more, faster. Python packages open up possibilities for: Advanced statistical analysis Machine learning implementation Automated reporting Complex visualizations API integrations Real-World Enterprise Application: Go-to-Market Analytics Open-source Python packages are designed to address your enterprise use cases so you don’t have to reinvent the wheel. Go-to-market analytics is a great example. Here are some real-world use cases where Python’s open-source packages can help with go-to-market analytic tasks. Automated Workflows By leveraging packages such as pandas, you can automate your go-to-market analytics workflows, including the following: Automated customer segmentation Cohort analysis automation Customer success health scores Recurring KPI tracking Financial reports Predictive... --- - Published: 2025-01-07 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/python-in-excel-for-retail-and-ecommerce - Categories: Perspectives For retail and e-commerce companies, leveraging customer data effectively is crucial to improving satisfaction, enhancing retention, and increasing sales. However, advanced analyses like sentiment detection, sales trend monitoring, and churn prediction often require tools beyond Excel, forcing teams to toggle between platforms and rely on additional software. With Python now integrated into Excel, these sophisticated techniques are available directly within your spreadsheets, combining Excel’s familiarity with Python’s analytical power. In this post, we’ll explore how Python in Excel can transform customer insights through real-world applications in sentiment analysis, sales trend analysis, and churn prediction. From identifying how customers feel about your products to predicting which subscribers might leave, each example demonstrates how Python’s capabilities can seamlessly enhance Excel’s functionality. Now, you can conduct complex analyses with ease, making data-driven decisions faster and more accessible than ever. To start using Python in Excel, simply type “=py(“, and an editor will open, allowing you to apply Python functions, access powerful libraries, and perform advanced analysis—all without leaving your spreadsheet. Whether analyzing customer reviews or predicting churn, Python in Excel brings data science within reach for teams of any skill level. Sentiment Analysis on Customer Reviews with Python in ExcelEvery day, customers leave reviews that offer valuable insights into their experiences, satisfaction levels, and even product improvements. However, manually analyzing large volumes of reviews can be time-consuming and subjective. Python in Excel provides an efficient solution by enabling sentiment analysis directly within your spreadsheets, allowing you to categorize reviews as positive, negative, or... --- - Published: 2025-01-06 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/new-release-miniconda-24-11-1 - Categories: News We are excited to announce the 24. 11. 1 release of Miniconda, Anaconda’s miniature version of the Anaconda Distribution installer. It includes: Python - the most widely used programming language for artificial intelligence, data science and machine learning conda - the open-source, cross-platform package and environment manager Miniconda is free to download, easy to install, and comes with free community support. The installer is pre-configured to access Anaconda’s public package repositories that include over 33,000 open-source AI, data science, and machine learning packages across seven different platforms. Download Miniconda 24. 11. 1 today and read the release notes for the full list of user-facing changes and packages in this release. Please note: The Miniconda installer is subject to Anaconda’s Terms of Service. Conda 24. 11. 1 Miniconda 24. 11. 1 ships with conda 24. 11. 1. The full release notes describe all of the user-facing enhancements and bug fixes captured in this release. Enhanced User Insights: Telemetry Feature A new Anaconda telemetry plug-in (conda-anaconda-telemetry) has been added that captures anonymous data on package installation and searches. Anonymous telemetry data is collected for the following commands: conda create, conda install, and conda search. This update is designed to help us understand how Anaconda users interact with conda environments and packages, enabling us to enhance the tools and resources you rely on every day. You can read more about this feature in the Anaconda documentation and the blog post announcement. Uninstallation improvements We've enhanced Miniconda's uninstallation steps to ensure a more thorough... --- - Published: 2024-12-20 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/synthetic-data-the-new-fuel-for-ais-rapid-evolution - Categories: News For decades, AI has relied on real-world data as its backbone, fueling everything from predictive text to autonomous vehicles. However, as the scale and complexity of AI systems have exploded, so too have the challenges in acquiring, curating, and safeguarding real-world data. Enter synthetic data—a transformative approach to dataset generation that addresses these challenges and opens entirely new frontiers for AI development. The Synthetic Data Revolution Synthetic data is artificially generated data that mirrors the properties of real-world datasets. Unlike traditional data, it can be tailored to specific needs, created in infinite quantities, and designed with built-in safeguards to respect privacy and fairness. While it may seem like a niche solution, synthetic data is rapidly becoming indispensable in the development and evaluation of cutting-edge AI models. One of the most compelling examples comes from the automotive industry. Companies like Waymo and Tesla rely on real-world data gathered from an array of sensors covering the vehicle plus synthetic data to simulate millions of driving scenarios that would be impractical, dangerous, or impossible to capture in the real world. From testing how an autonomous vehicle might react to a pedestrian jaywalking in heavy fog to simulating rare traffic conditions, synthetic data has become a cornerstone of the industry’s rapid advancements. Similarly, the healthcare sector is experiencing a synthetic data renaissance. Startups and research institutions are using generative models to create synthetic medical records, enabling the training of AI systems without compromising patient privacy. For example, synthetic CT scans and MRI data are... --- - Published: 2024-12-18 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/python-in-excel-cheat-sheet - Categories: Product Python in Excel opens a world of possibilities, from advanced data analysis to crafting stunning visualizations—all without leaving your spreadsheet. But whether you're just starting out or need a quick refresher, the amount of functionality can feel overwhelming. That’s why we created a handy Python in Excel Cheat Sheet. It’s designed to provide a quick reference for essential Python commands and workflows, making your data analysis smoother and faster. This cheat sheet is your go-to guide for everything from creating Python cells and using Pandas DataFrames to crafting pivot tables and applying custom functions. It highlights key shortcuts, like switching output modes or looping through data, so you can seamlessly integrate Python in Excel into your daily tasks. With clear, concise instructions, it’s perfect for beginners looking to explore Python in Excel or experienced users wanting an easy-to-access reference for their day-to-day work. Ready to streamline your workflow and take full advantage of Python in Excel? Download the cheat sheet now and supercharge your spreadsheets! If you’re looking for more Python in Excel resources, check out our Comprehensive List of Python in Excel Resources blog post filled with courses, certifications, communities, and more. If you’re new to Python in Excel, check out our on-demand webinar Introduction to Python in Excel. During this webinar, we cover Python in Excel basics, understanding data types and output modes, a brief introduction to Pandas DataFrames, custom functions, and a simplified Python charting experience using the Anaconda Toolbox for Excel. You can watch the webinar... --- - Published: 2024-12-17 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/introducing-enhanced-usage-insights-for-anaconda-packages - Categories: Product - Tags: open source At Anaconda, we continually strive to improve the user experience for our community and customers. To serve our community better, we’re introducing a new usage insights conda plugin for Anaconda Distribution and Miniconda. This update is designed to help us understand how Anaconda users interact with conda environments and packages, enabling us to enhance the tools and resources you rely on every day. This conda plugin will allow us to: Ensure the most-needed packages are always available. Optimize resource allocation to prioritize building and testing the packages that are most important to users. Identify trends to deliver a smoother, more efficient experience. We’re committed to transparency and user choice, so this feature is opt-out friendly and designed with your privacy in mind. In this blog post, we’ll explore what data is collected, how it benefits you, and how you can manage your data preferences. What Data is Being Collected? The new plugin collects anonymized data, using the existing Anaconda anon-usage token, about specific commands used in the conda environment, including: Installed packages: What packages are being installed or updated, including which are dependencies, and the specific versions of each package Searches: What packages users are searching for and whether or not they exist in Anaconda channels Crucially, no personally identifiable information is collected and this plugin is not designed to monitor Terms of Service compliance. Users are identified only through random, anonymous tokens, ensuring your privacy. You can read more about these anonymous tokens. Why Collect This Data? Enhanced usage... --- - Published: 2024-12-17 - Modified: 2025-07-11 - URL: https://10.2.107.56:8443/blog/unpacking-holiday-travel-trends - Categories: Product The travel industry is constantly changing, shaped by new technology, economic shifts, and global events. Tools like Google Flights, shifts in passenger demand, and fare changes all tell a story of how the industry adapts. In this analysis, we explore holiday air travel trends. Using data from public sources and tools like Panel, hvPlot, and Anaconda Notebooks, we developed visualizations like curves, bar charts, heatmaps, and boxplots to measure search interest, passenger numbers, flight schedules, airport activity, and fare changes between cities. Together, these pieces show how the industry handles the busy holiday season. This isn’t just about travel—it’s about finding patterns and making sense of big changes. By looking closely at the data, we can see how decisions are made and how trends unfold, offering insights that go far beyond holiday travel. Here are the sources we used:“Google flights” popularity: Google TrendsAviation Data on transportation. govBureau of Transportation StatisticsHere is the code: flights. ipynb on Anaconda NotebooksTrending: Google Flights Growth Over Time Since 2011, Google Flights has become a go-to tool for travel planning, and its search trends tell an interesting story. Before its launch, search interest was nearly nonexistent. Over the years, as more people discovered the convenience of planning trips online, searches for “Google Flights” steadily grew throughout the 2010s. The graph shows a sharp drop in 2020 when the COVID-19 pandemic brought global travel to a halt. But the recovery afterward has been remarkable. As airlines rebounded, people embraced new digital tools and felt a renewed... --- - Published: 2024-12-04 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-state-of-enterprise-open-source-ai - Categories: Perspectives AI and machine learning have become the engines of enterprise innovation, and open-source tools are the fuel. Whether it’s speeding up development, cutting costs, or unlocking new capabilities, open source is powering a shift in how businesses approach AI. But as these tools become more integral, they present new challenges. Companies must work to keep systems secure and manage performance at scale while ensuring teams have the skills to make the most of them. Our latest report, The State of Enterprise Open-Source AI, dives into how enterprises are balancing these forces. We’ve sifted through survey data from 100 IT decision-makers at large and midsize enterprises to shed light on the trends, challenges, and opportunities shaping the future of AI in the enterprise.   "Open-source AI is reshaping how enterprises innovate, offering tools that drive smarter decision-making and better operational efficiency. But as adoption grows, so does the need for strategies that balance experimentation with long-term stability and security,” says Peter Wang, Chief AI & Innovation Officer at Anaconda. “The challenge lies in building systems that not only solve today’s problems but scale seamlessly for the future. " Find highlights from the research below. Open-Source AI Adoption: The Current Landscape Open-source AI is becoming the backbone of enterprise innovation, with adoption rates reflecting its growing importance across industries. These findings spotlight sectors leading the charge and tools setting the standard. Reliance on Open Source: Over half (58%) of organizations use open-source components in at least half of their AI/ML projects, with... --- - Published: 2024-12-03 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-available-in-aws-marketplace - Categories: Technical Notes We are excited to announce that Anaconda is available in the AWS Marketplace. The partnership will allow AWS users to harness the reliability and security of Anaconda Distribution and Package Security Manager, with the ease of an up-to-date AWS Machine Image (AMI). AI development requires a complex combination of software and computing power. The AWS-Anaconda partnership combines the leader in on-demand cloud computing platforms and the world’s most trusted open-source software for data science and AI projects. Simplifying Data Science and AI Workflows Anaconda has been at the forefront of data science and AI by providing a secure, easy-to-install distribution of Python and R packages for over a decade. By partnering with AWS and listing on the AWS Marketplace, we are further simplifying the process for organizations to deploy Anaconda in their cloud infrastructure.   Why This Is a Big Deal The AWS Marketplace is known for its rigorous standards and the quality of products it hosts. Anaconda’s presence in the marketplace means you can easily integrate Anaconda into your AWS environments, allowing you to: Seamlessly deploy Anaconda: Launch Anaconda environments within AWS with just a few clicks. Scale effortlessly: Leverage AWS's scalable infrastructure to handle large datasets and complex computations without the need for extensive configuration. Enhance security: Combine Anaconda’s trusted distribution with AWS’s reliable security settings to ensure your data and analytics processes are protected. Optimize costs: Utilize AWS's flexible pricing to maintain cost-effective data science and AI operations.   Getting Started with Anaconda on AWS To get... --- - Published: 2024-11-27 - Modified: 2025-07-22 - URL: https://10.2.107.56:8443/blog/serve-holiday-code-ai-navigator-anaconda - Categories: Product With Thanksgiving upon us, we decided to put together a fun project that showcases how you can use generative AI in your coding work. Using Anaconda’s AI Navigator for coding assistance, we’ll demonstrate how to use various prompt engineering techniques that affect both the system and the user to provide a templated experience. By the end of this blog, you’ll learn how you can start dropping fun turkey eggs into your projects with generative AI. While this is a fun project, the skills demonstrated in this post highlight how generative AI can be used to unlock creative problem-solving and enhance your technical toolkit. How to pick the right model When choosing a model for developing a coding assistant, there are a few key factors to consider. First, ensure the model has been fine-tuned on relevant code datasets to understand programming languages and conventions effectively. Look for models with a strong track record in code generation, debugging, or explaining snippets across multiple programming languages. When selecting a model, it's also important to consider quantization—a process that reduces the precision of the model's parameters (e. g. , from 32-bit to 8-bit) to make it more efficient without significantly compromising performance. Quantization can drastically lower memory usage and improve inference speed, enabling larger models to run on less powerful hardware. For this project, I’ll use the OpenHermes-2. 5-Mistral-7B model from teknium. This model has been trained with code datasets that should work well for my use case of prompting an LLM to be... --- - Published: 2024-11-27 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/analyze-u-s-election-data-python-anaconda - Categories: Technical Notes The 2024 U. S. presidential election offers a rich dataset for data scientists to explore. Using the open-source Python libraries Panel, hvPlot, and HoloViews we created visualizations like Sankey diagrams, heatmaps, and box charts to illustrate key political dynamics, including voter turnout, partisan divisions, and swing state competitiveness. Explore the code used to build these visualizations in Anaconda Notebooks. These visualizations reveal patterns such as regional disparities in turnout and shifts in issue-based support, offering valuable insights into U. S. politics. Beyond elections, this project demonstrates how combining datasets and advanced visualization tools can simplify complex data, uncover actionable trends, and deliver impactful narratives for businesses and organizations. We sourced data for this project from the following:Poll data: https://apnorc. org/projects/ap-votecast/Margin of victory data: https://www. reddit. com/r/dataisbeautiful/comments/1ggt7hw/oc_how_many_times_has_your_state_had_a_final/Turnout rate data: https://election. lab. ufl. edu/data-archive/national/ Sankey data app: https://obliging-mandalay-cobra. anacondaapps. cloud/election_pollsTrump’s ElectabilityUsing data from AP VoteCast, we built a custom Sankey diagram app with HoloViews. Explore our dynamic Sankey app here and create your own analysis. Sankey diagrams are excellent tools for visualizing the relationships between two or more categories, making it easier to understand proportions, patterns, and the movement of values within a system. The app visualizes the responses to two questions:PARTYFULL: “Do you lean toward Democrats, Republicans, or neither? ”TRUMPTRAITSWIN: “Can Donald Trump win the general election in November? ”On the question of whether Trump can win the general election, Republicans overwhelmingly exude confidence, with the majority believing he can succeed. Democrats, on the other hand, are largely skeptical, leaning heavily... --- - Published: 2024-11-25 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/new-release-miniconda-24-9-2 - Categories: Technical Notes We are excited to announce the 24. 9. 2 release of Miniconda, Anaconda’s miniature version of the Anaconda Distribution installer, that includes: Python - the most widely used programming language for data science and machine learning conda - the open-source, cross-platform package and environment manager Miniconda is free to download, easy to install, and comes with free community support. The installer is pre-configured to access Anaconda’s public package repositories that include over 33,000 open-source data science and machine learning packages across seven different platforms. Download Miniconda 24. 9. 2 today and read the release notes for the full list of user-facing changes and packages in this release. Please note: The Miniconda installer is subject to Anaconda’s Terms of Service. Conda 24. 9. 2 Miniconda 24. 9. 2 ships with conda 24. 9. 2. The full release notes describe all of the user-facing enhancements and bug fixes captured in this release. Highlights include: As of v24. 9. 0, conda has made an important change to how it manages its default channel settings. Historically, conda was hardcoded to use Anaconda's channels, a remnant behavior from conda's early days when it was incubated at Anaconda. The conda community recently concluded that this behavior leads to a poor user experience because users may be unaware that they are using Anaconda’s channels when they don’t mean to. To provide a more flexible and transparent channel management experience, conda now requires users to explicitly specify their channels in their . condarc configuration file. Following this new... --- - Published: 2024-11-19 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-toolbox-bringing-enterprise-grade-python-analytics - Categories: Technical Notes We are thrilled to announce the General Availability of Anaconda Toolbox for Excel! Anaconda Toolbox for Excel is our comprehensive Excel add-in that brings critical data science tools directly to Excel users in the application they know and love. Starting today, organizations can take full advantage of Python in Excel and the Anaconda ecosystem with easy-to-use tools, featuring a completely redesigned visualization experience. What is Anaconda Toolbox? Anaconda Toolbox is an innovative Excel add-in that seamlessly integrates Python capabilities into Microsoft Excel, enabling users to leverage the power of Python analytics without leaving their familiar spreadsheet environment. At its core, Anaconda Toolbox for Excel transforms how organizations work with data by combining Excel's accessibility with Python's analytical prowess, all while maintaining enterprise-grade security and compliance. Enhanced Visualization Builder: A New Era of Data Visualization Visualize with Python, the redesigned feature that is the centerpiece of this release, brings unprecedented ease and flexibility in reducing the time to insight with powerful data visualizations using Python in Excel. We conducted user research interviews and leveraged domain knowledge from Excel MVPs to reimagine the ‘Visualize with Python’ interface, making it easier than ever to create sophisticated visualizations using Python's powerful libraries in a guided and intuitive way.   Figure 1: Faceted distribution charts offer multivariate analytics and insights   Key improvements to the Visualization Builder include: Streamlined Chart Creation: Choose from nine different chart types including Bar, Box, Count, Distribution, Line, Pairwise, Regression, Scatter, and Violin plots Interactive Design Controls: Fine-tune your visualizations... --- - Published: 2024-11-19 - Modified: 2025-07-08 - URL: https://10.2.107.56:8443/blog/anaconda-code-create-user-defined-functions - Categories: Technical Notes We’re excited to announce a powerful new feature in Anaconda Code: Python User-Defined Functions! With UDFs, you can write Python functions and use them just like native Excel functions, bringing the full power of Python’s rich data science ecosystem directly to your spreadsheet formulas. Let’s look at a real-world example that showcases how powerful this integration can be. From Cross-Tab to Long Format in One Function Call Consider a common data reshaping challenge: converting cross-tabulated (wide) data into a long format. You’re probably thinking, “Well, I can do that with Power Query no problem. ” Yes, true. But the resulting Table isn’t dynamic. Every time the input data changes, the query needs to be refreshed. And the query is tied to that one workbook. With Anaconda Code and Anaconda Toolbox, you can create a Python UDF, save it as a Code Snippet, and have it available in any workbook you open. Plus, the code needed to quickly unpivot a wide-format table is surprisingly simple. Here’s how you can create a custom UNPIVOT function that works just like any built-in Excel function: Python # decorate the function with the UDF decorator @UDF(name='UNPIVOT', nested=False) def unpivot_data( data: UDF. Range, # the input data from the Excel sheet id_vars, # the column(s) to keep fixed in the output var_name='variable', # the column name to hold the unpivoted column headers value_name='value' # the column name to hold the unpivoted values ) -> UDF. Range: # output the result as an Excel spilled array #... --- > Discover why Python became the #1 programming language. Learn its history, use cases, and why 47M developers choose it for AI and data science. - Published: 2024-11-11 - Modified: 2025-07-11 - URL: https://10.2.107.56:8443/blog/why-python - Categories: News What Is Python? Python is a high-level, interpreted programming language that has gained widespread recognition for its simplicity and readability. Released by Guido van Rossum in 1991, Python has evolved into a versatile, general-purpose language used in various applications, from web development to artificial intelligence. A Brief History of PythonPython began when Guido van Rossum started working on it as a hobby project during his Christmas holidays in 1989. The language was officially released in 1991, with its name inspired by the British comedy group Monty Python. Python has been in use since its release, with a particular increase in popularity in the mid-2000s, due to the rise of big data, machine learning, and social media — all areas where Python can be a useful solution for early development. Python has also inspired an active user community and many localized user groups. In 2000, Python 2. 0 was released, introducing important features like list comprehension and a garbage collection system. Eight years later, Python 3. 0 made its debut, focusing on removing duplicate programming constructs and modules to make the language more consistent. In 2020, Python 2 reached its end-of-life stage, which shifted the Python community’s focus entirely to Python 3. In 2022, Python overtook Java and C in popularity for the first time in 20 years. Common Use Cases for PythonPython’s versatility has led to its adoption across a wide range of industries and applications. In web and software development, Python powers many websites and applications that are used... --- > If you’re looking to improve your data analysis skills and are unsure whether you should learn Visual Basic for Applications (VBA) or Python in Excel, you’ll want to read this! - Published: 2024-11-06 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/vba-vs-python-in-excel - Categories: Technical Notes As a Microsoft Excel trainer, I speak regularly with professionals seeking to level-up their data analysis skills. Some of these professionals are looking to secure formal data roles (e. g. , data analyst/scientist), but most are looking to have more impact at work in their current role by improving their data analysis skills. One of the most common questions I receive from these professionals is whether learning Visual Basic for Applications (VBA) or Python in Excel is the best use of their limited time. Since you’re reading this blog, I will assume you have the same question. Let’s dig into it. First, here’s how to use this blog post most effectively: If you’re new to VBA, keep reading. If you’re familiar with VBA, feel free to skip ahead to Understanding Python in Excel. Understanding VBA VBA was added to Microsoft Excel back in 1993 and was an immediate hit, making it simple to automate manual steps in common business processes. VBA essentially turned Excel into an application development platform with the ability to streamline repetitive tasks. It’s no surprise why VBA code in Excel became a go-to tool of organizations both large and small. Even today, many business processes are not automated using IT systems – they still have one or more manual steps. It’s common to use Microsoft Excel as the “glue” between IT systems. For example, data is recorded and managed in Excel and then periodically loaded into an IT system. It’s also common to see entire business... --- - Published: 2024-11-05 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/dan-meador-free-book-chapter - Categories: Perspectives Building Data Science Solutions with Anaconda is a comprehensive starter guide to building robust and complete models. The book covers everything you need to know about algorithm families and helps you build must-have skills such as building interpretable models and avoiding bias in data. By the end of the book, you’ll be able to confidently use conda and Anaconda Navigator to manage dependencies, and you'll have gained a thorough understanding of the end-to-end data science workflow. This free chapter, "Dealing with Common Data Problems," covers the following topics: Dealing with too much data Finding and correcting incorrect data entries Working with categorical values with one-hot encoding Feature scaling Working with date formats Purchase the full book Download PDF About the Author Dan Meador"Professional nerd" is Dan Meador's response whenever anyone asks what he does for a living. It wasn't always so clear-cut when he played football for the Arkansas Razorbacks as he was getting his degree in computer engineering, but nowadays the pendulum has swung pretty clearly into the "nerd" classification. Over a decade working in Fortune 5 companies and later finding startups to be more liking, he's seen firsthand how the power of data can help ask better questions and guide better solutions. He holds a patent for his work on AI systems and has been able to grow his experience in AI/ML by building AutoML solutions. His journey has also taken him to the Pentagon where he was able to present his work on AI systems. Dan currently... --- > To help you find the packages and projects best suited to your needs, Anaconda provides a categorized view of the most popular and well-maintained packages built for performance, security, and more. - Published: 2024-11-05 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/how-to-pick-packages - Categories: Technical Notes With so many software packages available to you, it can be difficult to know which ones to download. To help you find the packages and projects best suited to your needs, Anaconda Cloud provides a categorized view of the most popular and well-maintained packages built for performance, security, and more. Package pages are maintained by page contributors. For that reason, some sections detailed below may not appear in all package pages. Accessing package pages Start your package exploration in Anaconda Cloud. 1. In Anaconda Cloud, navigate to the Packages tab. 2. Find packages and projects by category in the Package Categories page. Note that some packages match multiple categories. Search for packages in the local search bar. 3. Select a category to visit the category’s landing page. 4. Select a package to open its package page. You can click on any column header to sort the package list by that field. Package page overview You can think of package pages as hubs for learning about and using specific packages. There you’ll find a handy sidebar, a content page, and a how to’s page. Sidebar The sidebar provides a package description, usage stats, methods for installation, links to the package’s official documentation and related sites, and a list of page contributors. View package details sourced from GitHub (if available). Click Installation Instructions to open installation documentation. Copy and run install links to install the package in your environment. Visit official package documentation and other related sites. Content 4. Watch videos and... --- > Anaconda has updated the CLI from a monolith and a plugin architecture. - Published: 2024-10-30 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/product-update-anaconda-cli - Categories: Technical Notes Today Anaconda announces the transformation of the Anaconda CLI from a monolithic codebase into a plugin architecture. This will allow us to more easily maintain anaconda-client, providing stable community package management on anaconda. org, and allow a path for rapid innovation and access to a number of new and exciting features of our platform. Anaconda-client has been the backbone of the Anaconda Command Line Interface (CLI) since its initial creation in 2013. It is the workhorse responsible for uploading and managing packages on community channels hosted on anaconda. org, such as conda-forge, bioconda, and many others. After more than a decade of development, some of the early features and technical decisions have become technical debt. With this change, anaconda-client will evolve to become the anaconda. org-specific plugin into a larger Anaconda CLI ecosystem. Summary of Changes in Upcoming Release The first phase of this change was released in anaconda-client 1. 13. 0. This release, while providing a fundamental architectural shift, is intended to have minimal impact on user experience, particularly in automated CI/CD scenarios. Only minor changes to interactive login flows are changed, and only in certain cases. Some highlights: While the anaconda CLI entrypoint will be moved to a new package called anaconda-cli-base, the user experience will be nearly 100% backwards compatible The exception is triggered if another plugin such as anaconda-cloud-auth is also installed in the user’s conda environment, in which case the interactive login and logout commands will add an additional prompt to ask which domain to... --- > Code snippets, available through Anaconda Toolbox, allow time savings and more efficient workflows in Notebooks. Try it today! - Published: 2024-10-29 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/the-power-of-code-snippets - Categories: News In the fast-paced world of data science, efficiency and speed are paramount. That's where the magic of code snippets comes into play. These small blocks of reusable code can significantly expedite the coding process, allowing data scientists and analysts to focus on the bigger picture rather than wasting time with repetitive tasks. Imagine having a personal repository of code snippets at your disposal, ready to be used with just a click. This is a reality that can be achieved through the use of the new Code Snippets feature in Anaconda Notebooks. By saving code from any cell in your notebook, you create a library of snippets that can be easily accessed and reused across various projects. The beauty of code snippets lies in their simplicity and versatility.   Whether it's importing essential libraries at the start of every notebook. import pandas as pd import numpy as np Loading datasets via csv: df = pd. read_csv Or formatting dates in your preferred style: df = pd. to_datetime(df). dt. strftime('%d-%b-%Y') These snippets save invaluable time. Moreover, snippets are not limited to data manipulation. They extend to visualization as well, with chart templates, ready to turn raw data into insightful graphs. fig. add_trace(go. Scatter(x=X, y=y1, mode='lines', name=y1. name, line=dict(color='green', width=2))) And for those who love to customize their user interfaces, snippets for Panel components: button = pn. widgets. Button(name='Click me', button_type='primary') Even the aesthetics of your code can be enhanced with snippets for Markdown formatting, ensuring that your notebooks are not only functional... --- - Published: 2024-10-24 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/new-release-anaconda-distribution-2024-10 - Categories: Technical Notes We are excited to announce the 2024. 10 release of Anaconda Distribution, Anaconda’s data science distribution installer, which includes: Python - the most widely used programming language for data science and machine learning conda - the open-source, cross-platform package and environment manager Anaconda Navigator - our desktop application, built on conda, that enables you to launch notebooks and development applications from your managed environments And over 300 additional, automatically-installed packages that have been tested together to work “out of the box” Anaconda Distribution is free to download, easy to install, and comes with free community support. The installer is pre-configured to access Anaconda’s public package repositories that include over 33,000 open-source data science and machine learning packages across seven different platforms. Download Anaconda Distribution 2024. 10 today. The full release notes can be found here. The Anaconda Distribution installer is subject to our TOS, which you can learn more about here. Python 3. 12. 7 Anaconda Distribution 2024. 10 ships with Python 3. 12. 7. Package Updates Python 3. 12. 7 is included in the base environment, and key package updates include: Dask 2024. 8. 2 Jupyterlab 4. 2. 5 Matplotlib 3. 9. 2 Numba 0. 60. 0 Numpy 1. 26. 4 Pandas 2. 2. 2 Scikit-Image 0. 24. Scikit-Learn 1. 5. 1 SciPy 1. 13. 1 Spyder 5. 5. 1 See the complete package lists, and explore our Anaconda Distribution 2024. 10 metapackages, with builds for Python 3. 8, 3. 9, 3. 10, 3. 11, and 3. 12. (Please... --- - Published: 2024-10-24 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/analyze-halloween-data-ai-python - Categories: Technical Notes For Halloween this year, we decided to look into what consumer economic data can tell us about how Americans celebrate the holiday. Using a combination of multimodal AI tools and open-source Python packages, we demonstrate how you can streamline the data analysis workflow. To examine all of the code that went into this project, check out this Anaconda Notebook. Using Perplexity to search for data But first... we need data! Let’s use LLMs to help, starting with Perplexity, a free AI search engine. I asked it: “Where can I find a Halloween dataset” and some of the links I found from Perplexity and a bit of diving into its results include: https://nrf. com/research-insights/holiday-data-and-trends/halloween/halloween-data-center https://fred. stlouisfed. org/series/PCU31133113 https://fred. stlouisfed. org/series/IPG3113N https://github. com/fivethirtyeight/data/tree/master/candy-power-ranking The first link is the NRF (National Retail Foundation) which has been conducting its annual Halloween survey with Prosper Insights & Analytics for over a decade to see how Americans celebrate Halloween. The second (two) links are from FRED (Federal Reserve Bank of St Louis), which tracks the Producer Price Index by Industry: Sugar and Confectionery Product Manufacturing & Industrial Production: Manufacturing: Nondurable Goods: Sugar and Confectionery Product, respectively. The final link is from FiveThirtyEight, which started a candy popularity contest that saw thousands of participants take part in 269,000 randomly generated candy matchups. Using multimodal models to scrape data We now have data sources, but unfortunately, not all of them are machine-readable — sure there are pictures and graphs that are easily interpretable by humans, but not really... --- - Published: 2024-10-23 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/new-environment-management-capabilities-in-public-beta-on-package-security-manager - Categories: Product We are excited to introduce the environment management feature on Package Security Manager (PSM) on Cloud! This new environment management functionality enables data scientists to automatically back up their conda environments and get valuable security information whenever an environment changes. For the IT administrator, the environment management feature provides observability into all of your organization's conda environments and their security vulnerabilities. Giving administrators more control over the environments in their organizations and the ability to take advantage of new insights enabling better organization wide observability and security. The new environment features on Package Security Manager include: An Easy-to-Use Interface The user-friendly interface makes managing environments simpler and more intuitive, saving time and reducing complexity for users. With the simple click of a button, any user can archive or quickly scan environments. Within each environment, you can easily view the creation date, last updated date, associated CVEs, and the number of packages. Identify Security Issues With environment vulnerability scanning, users can detect and address potential security threats within their environments, reducing risk and enhancing overall project security. Organization’s environments can also be customized to your team’s unique compliance policy, keeping IT teams happy. Better Administrative Insights Each active and archived environment will include the number of packages and the types of packages being used, giving administrative users more visibility into user and environment data. Administrative users will also have the ability to set the status of an environment to “Okay, Warn, or Block” and notify the creator of the environment of... --- > Learn how Marketing teams can take advantage of Python in Excel's advanced data analysis capabilities. - Published: 2024-10-23 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/python-in-excel-for-marketing - Categories: Product For years, Excel has been the go-to tool for marketers and analysts, providing powerful capabilities for organizing data or generating reports. However, when it comes to more advanced data analysis tasks, Excel alone is not enough. Marketers often have to turn to third-party add-ins or entirely different platforms to get the job done. That can change with the integration of Python in Excel. This powerful combination brings the advanced data-processing capabilities of Python directly into the familiar Excel interface, creating a seamless workflow for marketers and data analysts. You can now run sophisticated analyses (customer segmentation, churn prediction, or sentiment analysis) using Python. No more switching between platforms, exporting and importing data, or relying on clunky add-ins. Python brings a whole new level of functionality to Excel, unlocking advanced techniques that were previously out of reach. In this blog post, we’ll explore several real-world examples of how Python in Excel can transform marketing analysis. From analyzing qualitative data like customer reviews to predicting customer churn and segmenting customers with clustering algorithms, we will show how these tools work in practice. By leveraging Python's powerful libraries,marketers can uncover deeper insights, make data-driven decisions, and ultimately, achieve better business outcomes from a tool they already know and love. To get started with Python in Excel, simply type ‘=PY(’. As soon as you type the open parenthesis, a Python editor will appear within Excel. This integrated editor allows you to easily select cells, apply Python functions directly to your data, and access Python... --- - Published: 2024-10-23 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/mastering-prompt-engineering-llms - Categories: Product In the rapidly evolving field of artificial intelligence, prompt engineering has emerged as a crucial skill for developers, data scientists, and researchers working with large language models (LLMs). As we harness the capabilities of models like GPT-4, understanding the fundamentals of prompt engineering, key techniques to use, and best practices to follow can help you unlock the full potential of LLMs. What is prompt engineering? Prompt engineering is the art and science of designing input prompts that elicit desired responses from LLMs. It involves crafting precise instructions, providing context, and structuring queries to guide the model toward generating accurate, relevant, and useful outputs. Essentially, it's about communicating effectively with AI to achieve specific goals—whether that means answering a question, generating creative content, or extracting insights from data. Why Prompt Engineering Matters Large language models are incredibly powerful, but they are highly dependent on the prompts users provide. A well-crafted prompt can lead to insightful, accurate responses, while a poorly designed one may result in irrelevant or misleading outputs. Prompt engineering bridges the gap between human intent and machine understanding, enabling more effective and controlled use of these models across a variety of applications—from automating workflows to generating novel content. The impact of well-crafted prompts is clear in a study by Anthropic on contextual retrieval, where combining specific techniques reduced retrieval failure rates significantly. Similarly, IBM emphasizes that well-designed prompts help ensure AI responses are accurate and pertinent, directly improving user satisfaction. Key Techniques and Patterns in Prompt Engineering Here are... --- > Implementing channel-level policies in Anaconda’s Package Security Manager will help streamline your package management process and keep your organization's repositories secure against vulnerabilities. - Published: 2024-10-21 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/implementing-policies-at-the-channel-level-on-package-security-manager - Categories: Technical Notes There are currently three primary methods for integrating packages into a channel on Package Security Manager (PSM) on prem: Creating mirrors Copying/moving packages from another channel Manual uploads However, these current methods have some limitations regarding security filtering and their ability to be replicated across multiple channels. How Package Management Works Now Most PSM users rely on mirroring to bring packages into their repositories and keep their repositories updated by scheduling mirror updates on a daily, weekly, or monthly basis. A Common Vulnerabilities and Exposure (CVE) mirror, on the other hand, runs every four hours, ensuring timely updates on vulnerabilities. Despite this, a critical gap remains: if a new CVE is identified, vulnerable packages can still exist in the repository until the next scheduled mirror runs. This creates a window of opportunity for serious security risks, as users may inadvertently install these vulnerable packages. Administrative users can manually move vulnerable packages to a restricted channel or delete them before the next mirror cycle but this can be time consuming. Introducing Policy Filters Users will now have the ability to create policy filters, which will allow for more robust security measures. Figure 1: When creating a policy, administrators can add specific package rules and apply them to specific platforms, licenses, and other package criteria. Here is how it will work: Policies will be applied at the channel level, enabling administrators to set specific rules—such as allowing only packages with a CVE score below 7. This can be applied across multiple channels,... --- - Published: 2024-10-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/python-3-8-reaches-end-of-life - Categories: Product As part of our commitment to maintaining up-to-date and secure Python environments, Anaconda is announcing the end of support for Python 3. 8 in our main channel of the Anaconda Distribution. This decision aligns with the upstream CPython support cycle for Python minor versions. Key Dates End of Support Date: October 31, 2024 Last Security Fix: October 4, 2024 (python-3. 8. 20 was the last security fix release to main for Python 3. 8) What This Means for Users After October 31, 2024: Availability: All Python interpreter packages for Python 3. 8 (e. g. , python=3. 8. *) will remain available in the main channel. Packages built to work with Python 3. 8 (also known as Python 3. 8 "variants", which include py38 in the filename) will remain available in the main channel. No Further Updates: No additional patch releases for Python 3. 8, including security fix releases, will be provided by the upstream CPython project or Anaconda. Anaconda will stop building and distributing Python 3. 8 variant packages. Recommendations for Users If you're still using Python 3. 8, we strongly recommend upgrading to a more recent version of Python (e. g. , 3. 9, 3. 10, or later) to ensure you receive the latest security updates and can take advantage of new features. Review your projects and dependencies to plan for migration to a newer Python version. Test your code with newer Python versions to identify and address any compatibility issues. Upgrading We understand that upgrading can sometimes be... --- - Published: 2024-10-09 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/a-comprehensive-list-of-python-in-excel-resources - Categories: Product Are you new to Python in Excel or looking to take your skills to the next level? We’ve created this Resource Guide to help you make the most out of Python in Excel and take advantage of all it has to offer. From certification courses, to video tutorials, to Python in Excel communities you can be a part of, these resources help you no matter where you are on your Python in Excel journey. We will be updating this blog post as new resources become available so be sure to bookmark this page! Courses and Certifications Anaconda Certified: Data Analysis with Python in ExcelThis program will leverage your Excel knowledge to jumpstart your data analysis skills using pandas, one of the most popular Python packages for working with data. Anaconda’s Machine Learning with Python in Excel CourseIn this hands-on course, you’ll learn how to use Python in Excel to create simple machine-learning experiments by working on coding examples in a live-coding environment. LinkedIn’s Python in ExcelLearn how to manage your entire data workflow directly inside an Excel worksheet. The content covered includes referencing Excel values in Python, using pandas to analyze data in an Excel worksheet, using Python plotting modules, and more. LinkedIn’s Python in Excel: Getting Started with Data AnalysisThis hands-on course covers the basics of working with data using Python in Excel including data structures, DataFrames, functions, and more. Throughout the course a handful of practical Python-in-Excel examples such as fixing dates with dateutil, generating a random sequence,... --- - Published: 2024-09-26 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/python-in-excel-for-finance - Categories: Technical Notes Traditionally, finance professionals have relied heavily on Microsoft Excel as a go-to tool for tasks like financial modeling, budgeting, forecasting, and data analysis. Excel’s strength lies in its versatility and ability to handle large datasets, perform complex calculations, and create reproducible models that are easily shared across teams. Whether it's used for managing budgets, forecasting revenue, or building intricate financial models, Excel has been the cornerstone of day-to-day financial operations. Its user-friendly interface, combined with powerful features like pivot tables, data visualization tools, and a wide range of built-in functions, has made it indispensable in the finance world. In a similar manner, Python has emerged as one of the dominant programming languages in finance, thanks to its robust, out-of-the-box functionality and vast array of libraries tailored for financial analysis. Python's flexibility and extensive library ecosystem, including pandas, NumPy, and Matplotlib, have empowered finance professionals to conduct sophisticated data analysis, automate repetitive tasks, and create dynamic financial models. Python’s ability to perform data manipulation and integrate seamlessly with other systems has made it a favorite among those looking to push the boundaries of financial analysis. What if we could combine Excel's accessibility with the batteries-included mentality of Python? Now, you can with Python in Excel. By simply typing ‘=PY’ Excel users can integrate Python directly into their spreadsheets. This fusion allows users to leverage Python’s advanced capabilities while continuing to work within the familiar environment of Excel. The integration of Python into Excel means that tasks that previously required external scripts... --- - Published: 2024-09-18 - Modified: 2025-07-23 - URL: https://10.2.107.56:8443/blog/update-on-anacondas-terms-of-service-for-academia-and-research - Categories: Product Please note this post is outdated. Our current Terms of Service policies can be reviewed here. Recently, our users at academic and research institutions have raised important questions about commercial fees in Anaconda’s Terms of Service and their applicability in educational settings. We recognize that an update to the Terms of Service in March 2024 was not communicated as clearly as it should have been, causing confusion, concern, and unintentional exclusion of a large number of free users, particularly among our valued academic and research users. We take full responsibility for this lack of clarity and want to address it head-on. Anaconda was founded with a vision to drive the adoption and growth of open-source tools for numerical computing, science, and data analytics. From the beginning, academic and research institutions have been at the heart of our ecosystem and community. It’s clear that our current legal language doesn’t adequately reflect this commitment, and as such, we’re preparing an update to our Terms of Service, End User License Agreement, and other related documents. Once updated, these documents shall continue to reflect our commitment to our valued academic and research users by, for example, reiterating that Anaconda remains free for students, researchers, and educators at accredited universities, with very limited exceptions. This update, expected to conclude by year-end, aims to make our legal terms more accessible and understandable for our entire community. As we work to ensure our Terms of Service complement our intent within the community, we are providing the following... --- - Published: 2024-07-31 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-anaconda-toolbox-makes-python-in-excel-as-easy-as-py - Categories: Product We’re excited to announce new tools in our Anaconda Toolbox! These new tools, as well as existing ones, were created to help you quickly generate code and visualizations while also learning Python as you go. Anaconda Toolbox is powered by Anaconda—the same team that provides Python libraries for Microsoft’s Python in Excel. Python in Excel beta users can sign up and start using Anaconda Toolbox today. What Capabilities Does Anaconda Toolbox Offer to Excel Users? Anaconda Toolbox enables anyone, regardless of their Python experience level, to quickly generate code and visualizations in Microsoft Excel while learning Python along the way. By using Anaconda Toolbox, Python in Excel users can take full advantage of all the capabilities Microsoft Excel and Python have to offer. Plus, you can share data and collaborate with Python experts in Anaconda. cloud notebooks. Here are some of the key features of Anaconda Toolbox: Write, Save, and Share Code Snippets Code snippets allow you to easily carry code from place to place. Not only can you save your code to use at a later time, but you can also share code with other users directly through the Toolbox, without having to leave Microsoft Excel. The ability to write, save, and share code through Anaconda Toolbox saves you time and allows for better collaboration between users. Anaconda Assistant for Python in Excel Know what you want to do, but don’t know how to do it in Python? Just ask Anaconda Assistant. Use one of our provided prompts or... --- - Published: 2024-07-31 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/introducing-anaconda-code-add-in-for-microsoft-excel - Categories: Product Excel and Python users can now run their Python-powered projects in Excel locally with the Anaconda Code add-in “I wish there was a way for me to run my Python in Excel locally, without having to run my calculations through Microsoft Cloud. ” Since Python in Excel was introduced in August 2023, a clear piece of feedback we've heard from the community was the desire to run Python locally rather than through Microsoft Cloud, which is what happens when you use Python in Excel. Today we are launching the public beta of Anaconda Code, which allows users to run Python code locally in Excel. The technology behind Anaconda Code is powered by PyScript, an Anaconda supported open source project that runs Python locally without install and setup. If you want to learn more about PyScript, visit pyscript. net. By bridging the gap between traditional spreadsheet use and advanced coding practices, this solution grants users access to a wider Python ecosystem, enhancing data analysis capabilities while maintaining Excel's core strengths. Users can start using Anaconda Code today by visiting AppSource, Microsoft’s add-in marketplace, and downloading the Anaconda Toolbox. What are the Key Differences Between Python in Excel and Anaconda Code? For users looking for more control over their environments in Microsoft Excel or faster performance, Anaconda Code is a great option for running Python code while in Excel. Some other key differences include: Cells Run Independently In addition to running Python code cells in “row major order” - meaning any cells... --- - Published: 2024-07-26 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/analyzing-olympic-game-data-anaconda - Categories: Technical Notes Olympics as Charts The Olympic Games have a rich history dating back to ancient Greece, but the modern Olympics, as we know them, have evolved significantly since its beginning. With the Paris Olympics 2024 on the horizon, let’s visualize its history! Before we dive in... The dataset was downloaded from Kaggle Olympic Games Medals. The plots were created using hvPlot / HoloViews with the Bokeh backend using the light_minimal theme and careful curation. The notebook and code can be found on Anaconda Notebooks. When Did the Olympic Games Take Place and How Often? The frequency and occurrence of the Olympic Games have been influenced by numerous historical events. The plot below provides a visual representation of the Olympic Games from 1896 to the present day. The Olympic Games were canceled in 1916 due to World War I. The first Winter Olympics were held in Chamonix, France, in 1924. Both the 1940 and 1944 Olympic Games were canceled due to World War II. Initially, the Winter and Summer Olympics were held in the same year. However, starting in 1994, the International Olympic Committee (IOC) decided to stagger the events, holding the Winter Olympics and Summer Olympics in separate years. Who hosted the most Olympic Games? The chart below provides a visual representation of the number of times each country has hosted the Summer and Winter Olympic Games. United States (8 games) Summer Games: St. Louis (1904), Los Angeles (1932, 1984), and Atlanta (1996). Winter Games: Lake Placid (1932, 1980) and Salt... --- - Published: 2024-07-24 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda-accelerates-ai-development-and-deployment-with-nvidia-cuda-toolkit - Categories: Product We are pleased to announce that NVIDIA CUDA Toolkit 12 is now available on our main (AKA, defaults) channel, a significant update from our support for the previous version, CUDA Toolkit 11. 8. CUDA serves as the software layer for NVIDIA GPUs. The CUDA Toolkit gives users a development environment for building high-performance, GPU-accelerated applications. It enables developers to create, enhance, and implement applications across a wide range of platforms, including GPU-accelerated desktops, cloud platforms, and more. Previously, Anaconda distributed runtime libraries (such as cudart, cublas, and cusolver), the components of CUDA Toolkit that are required to run CUDA-enabled software. With this update, Anaconda additionally distributes the compilation and development tools (such as nvcc, nvrtc, cccl, and nsight) that are used to develop the software. Components in the CUDA Toolkit are now packaged individually, enabling users to fetch only the necessary components, saving them time and hard disc space. Through Anaconda, users can manage both Python packages and non-Python software in one environment. This means they can leverage the benefits of our other GPU-accelerated packages without needing to spend time figuring out how to get system-related and low-level software like CUDA to work. And developers of the software that runs on NVIDIA GPUs can now easily develop and test CUDA-enabled software in a conda environment, managing CUDA tools and libraries alongside other low-level dependencies and focusing on development rather than on getting their system to work. With more than 5,000 packages in Anaconda’s repositories, developers working on AI initiatives can... --- - Published: 2024-07-16 - Modified: 2025-07-08 - URL: https://10.2.107.56:8443/blog/introducing-evaluations-driven-development - Categories: Product At Anaconda, we’ve developed a rigorous new approach to AI development called Evaluations Driven Development (EDD). By continuously testing AI models using real-world cases and user feedback, EDD enables us to create AI assistants that are reliable, relevant, and truly impactful for users. Our Anaconda Assistant, an AI coding companion for data scientists, exemplifies the power of EDD. Trained on real code samples, errors, and fixes, it offers in-context suggestions and debugging help to supercharge your Python workflow. And thanks to EDD, it keeps getting smarter with each update. We believe EDD is the future of AI development, ensuring that AI tools don’t just demo well, but deliver real value. If you’re excited by AI’s potential but wary of the hype, read on to learn how EDD works and why it’s a game-changer for building AI applications that make a difference. The Anaconda Assistant: Your AI-Powered Data Science SidekickAs a data scientist working with Python, you know the frustration of hitting roadblocks in your code. That’s where the Anaconda Assistant comes in. Leveraging state-of-the-art language models trained on real-world Python code, errors, and solutions, the Assistant offers a range of features to streamline your workflow:Generating complex code snippets with clear explanationsSuggesting improvements to code style and readabilityProviding in-context explanations for functions and modulesRecommending data preprocessing techniques tailored to your projectBut the Assistant’s most popular feature by far is intelligent debugging. Telemetry data shows that 60% of user interactions involve asking for help with pesky errors. Just describe your error to... --- > Discover AI Navigator, Anaconda’s new desktop application for accessing and experimenting with over 200 local LLMs. Join our public beta and download AI Navigator today! - Published: 2024-07-01 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/blog/introducing-anaconda-ai-navigator - Categories: News, Product Editor’s Note: As of Sept. 30, 2024, Anaconda AI Navigator is now officially in GA. Learn more hereThe Anaconda team is thrilled to announce the public beta launch of AI Navigator, Anaconda’s newest product designed to bring the power of large language models (LLMs) directly to your desktop. Getting started is as simple as downloading AI Navigator to become part of Anaconda’s public beta for free and begin exploring its capabilities. What is AI Navigator? AI Navigator is a new desktop application from Anaconda that enables you to browse, download, and run Generative AI Models directly on your device. It features a user-friendly interface to guide you through our catalog of LLMs with various parameter counts, sizes, and capabilities so you can find the right model for your specific device—in a secure and private desktop environment. This tool allows you to experiment with and leverage these models to meet your specific use cases and organizational requirements. Whether you are a beginner or a seasoned professional in AI, AI Navigator opens up new possibilities for innovation and efficiency. AI Navigator is closely aligned with the Anaconda Navigator desktop application, used by millions of data practitioners to create, manage, and launch the data science and machine learning applications that they use every day. Making data science and machine learning tools and applications accessible, easy to use, and secure has always been the mission for Anaconda, and we now look to bring that same ease of access, ease of use, and security to... --- > The latest release of Anaconda Distribution, Anaconda’s free data science distribution installer, is now live. - Published: 2024-06-27 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/new-release-anaconda-distribution-2024-06 - Categories: News We are excited to announce the 2024. 06 release of Anaconda Distribution, Anaconda’s free data science distribution installer, which includes: Python - the most widely used programming language for data science and machine learning conda - the open-source, cross-platform package and environment manager Anaconda Navigator - our desktop application, built on conda, that enables you to launch notebooks and development applications from your managed environments And over 300 additional, automatically-installed packages that have been tested together to work “out of the box” Anaconda Distribution is free to download, easy to install, and comes with free community support. The installer is pre-configured to access Anaconda’s public package repositories that include over 33,000 open-source data science and machine learning packages across seven different platforms. Download Anaconda Distribution 2024. 06 today. The full release notes can be found here. Python 3. 12. 4 Anaconda Distribution 2024. 06 ships with Python 3. 12. 4. Package Updates Python 3. 12. 4 is included in the base environment, and key package updates include: Numpy 1. 26. 4 SciPy 1. 13. 1 Matplotlib 3. 8. 4 Pandas 2. 2. 2 Scikit-Learn 1. 4. 2 Scikit-Image 0. 23. 2 Dask 2024. 5. 0 Spyder 5. 5. 1 See the complete package lists, and explore our Anaconda Distribution 2024. 06 metapackages, with builds for Python 3. 8, 3. 9, 3. 10, 3. 11, and 3. 12. Conda 24. 5. 0 Anaconda Distribution 2024. 06 ships with conda 24. 5. 0, which contains several user-facing enhancements and bug fixes. You... --- - Published: 2024-06-26 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/accelerating_ai_development_and_deployment_with_lenovo - Categories: News In an exciting move, Lenovo, the world’s #1 PC and #1 Top500 supercomputer manufacturer and Anaconda announce a strategic partnership designed to supercharge AI development for practitioners worldwide. This collaboration brings together the powerful Navigator and Lenovo ThinkStation & ThinkPad P Series workstations, to create an unparalleled environment for accelerating AI workflows from conception to production. Tailored for Local Workloads Lenovo ThinkStation & ThinkPad P Series workstations are powered by high-performance Intel® Xeon® and Intel® Core™ Ultra processors and are known for their extreme performance and class-leading reliability, making them the perfect hosts for Anaconda's Navigator desktop platform. Lenovo’s Intel powered workstations and Anaconda deliver the perfect combination for AI practitioners, developers, and data scientists as well as the very best end-user experience for complex data science, machine learning, and generative AI workflows. AI is a journey, not a destination, so it is crucial to invest in a high-performance, secure, reliable, and scalable AI solution that supports the pace and direction of your AI strategy, from development to distribution. Navigator: Your AI Compass Navigator serves as the preferred platform for data scientists and AI practitioners. With a straightforward installation and easy-to-use and intuitive platform, developers can start coding immediately. Advanced users can also use the comprehensive flexibility of the command-line interface. Anaconda provides instant access to a wide range of tools and packages essential to any AI workflow. Users can seamlessly work from their preferred Integrated Development Environment (IDE), whether it’s Jupyter Notebook, Spyder, or RStudio. This integrated workflow empowers... --- - Published: 2024-06-25 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/blog/anaconda_recognized_in_insidebigdatas_impact_50_list - Categories: News We're thrilled to announce that Anaconda has been named one of the most impactful companies in the industry by insideBIGDATA, securing the 19th position on their prestigious IMPACT 50 List. This recognition underscores our commitment to democratizing access to cutting-edge data science and AI tools. insideBIGDATA, a trusted source for news and insights in the world of Big Data, AI, and machine learning, curates this list quarterly using advanced machine learning techniques to analyze industry metrics and determine the most influential players in the field. Our inclusion in this list reflects the significant strides we've made in expanding access to secure Python and R packages. Since 2022, we've seen an impressive 839% surge in enterprise usage of our Python packages, with our developer community growing to 1. 8 million strong. Key partnerships have played a crucial role in our growth and impact: Microsoft Excel Integration: In August 2023, we announced the beta availability of the Anaconda Distribution for Python in Excel, with the GA release expected later this year. This integration empowers users to leverage Python's capabilities within the familiar Excel environment. Teradata Collaboration: Our partnership with Teradata, announced in April 2024, brings popular Python and R packages to Teradata VantageCloud through the Anaconda Repository. This integration with ClearScape Analytics enables enterprises to deploy large-scale data science, AI/ML, and generative AI use cases more effectively. These collaborations, along with our partnerships with other industry leaders like IBM watsonx. ai, Lenovo, and Oracle Cloud Infrastructure, demonstrate our ongoing commitment to making... --- - Published: 2024-04-15 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/anonymous-usage-data-collection-in-miniconda - Categories: Product Last September, we announced the inclusion of anaconda-anon-usage in two of our signature products: Anaconda Distribution and Anaconda Navigator. That successful rollout has led to our decision to now include it in Miniconda. In this article, we detail why we’re doing this, how this data is being used, what data will be collected, where and when this update is happening, and how you can disable the feature. Why are we adding anaconda-anon-usage to Miniconda? Since September 2023, data collected from anaconda-anon-usage have enabled us to explore how a subset of our users interact with our products and repositories, primarily those who used the Anaconda Distribution installer, Anaconda Navigator, or our command line tool anaconda-client. We’ve learned: We have recorded over 6. 8 million installations of anaconda-anon-usage. 63% of installations use only one conda environment (i. e. “base”), perhaps representing an opportunity to better educate users on conda’s environment creation and management capabilities. Just 0. 6% of users have disabled anaconda-anon-usage, indicating an openness among our user base to share anonymous usage data to improve our products and their experience. Anaconda has also begun to leverage the data to think about how we might better serve our users and customers to navigate the open-source software landscape. For example, when a vulnerability is reported for specific versions of a package, we can track the number of active environments that contain the vulnerable packages and the proportion of those environments that upgrade to a newer version that has patched the vulnerability. In the... --- - Published: 2024-04-01 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/blog/anaconda-not-effected-by-malicious-xy-code - Categories: News What Happened? A March 29, 2024 announcement brought to light malicious code that affects the latest version of the “xz” tools and libraries. This was an identified level 10 severity CVE. We want to take this opportunity to reassure you that Anaconda products and packages were not impacted by this incident and our customers are safe from this issue. Why are Anaconda products and packages unaffected? Data Science and AI Workbench (AE5/DSP) and Package Security Manager (Server) Anaconda uses RHEL for the UBI base images to build Workbench and Package Security Manager. RedHat has confirmed that RHEL is unaffected. The vulnerability is present in certain Fedora releases. Anaconda. org The affected xz library versions (5. 6. 0 and 5. 6. 1) are not present in any of the following anaconda, main, and conda-forge channels. https://anaconda. org/anaconda/xz https://anaconda. org/main/xz https://anaconda. org/conda-forge/xz (not managed by Anaconda, but hosted on anaconda. org) Based on available information as of April 1st, 2024, only xz's 5. 6. 0 and 5. 6. 1 source artifacts are affected, and as a result, Anaconda's products are not known to be susceptible to this backdoor vulnerability. However, as this is an ongoing investigation in the software security community and we currently cannot be 100% certain that no other xz releases or other projects were affected; but rest assured that Anaconda will continue to update our customers and community of any further developments. To learn more about how the conda-forge community responded to this issue, see the blog article they... --- - Published: 2024-02-08 - Modified: 2025-07-06 - URL: https://10.2.107.56:8443/blog/visual-data-analysis-with-python-in-excel-using-bar-charts - Categories: Product This is the fourth in a series of blog posts that teach you to analyze data using Python code in Microsoft Excel visually. If you are new to Python in Excel, you should start with my Python for Excel Analysts blog series, which covers many concepts that will be assumed in this blog series. This series will use the Microsoft Excel Labs Python Editor to write code. However, the Python Editor is not required. All code can be entered using the Formula Bar and the new PY function. Each post in the series has an accompanying Microsoft Excel workbook to download and use to build your skills. This post’s workbook is available for download here. For convenience, here are links to all the blog posts in this series:Part 1 – Using HistogramsPart 2 – Using Box PlotsPart 3 – Using Scatter PlotsPart 4 – Using Bar Charts (this post)Part 5 – Using Line ChartsNote: To reproduce the examples in this post, install the Python in Excel trial. If you like this blog series, check out my self-paced certification program, Anaconda Certified: Data Analysis with Python in Excel. The Analysis ScenarioThis blog post will continue the hypothetical scenario started in Part 3 – analyzing the impact of promotion strategies. The following table of reseller sales data is included in the workbook for this blog post: Fig 01 – Reseller Sales Data The table in Fig 01 illustrates a very common scenario in business analytics – the extensive use of categorical data... --- - Published: 2024-02-08 - Modified: 2025-07-06 - URL: https://10.2.107.56:8443/blog/visual-data-analysis-with-python-in-excel-using-line-charts - Categories: Product This is the fifth and final in a series of blog posts that teach you to analyze data using Python code in Microsoft Excel visually. If you are new to Python in Excel, you should start with my Python for Excel Analysts blog series, which covers many concepts that will be assumed in this blog series. This series will use the Microsoft Excel Labs Python Editor to write code. However, the Python Editor is not required. All code can be entered using the Formula Bar and the new PY function. Each post in the series has an accompanying Microsoft Excel workbook to download and use to build your skills. This post’s workbook is available for download here. For convenience, here are links to all the blog posts in this series:Part 1 – Using HistogramsPart 2 – Using Box PlotsPart 3 – Using Scatter PlotsPart 4 – Using Bar ChartsPart 5 – Using Line Charts (this post)Note: To reproduce the examples in this post, install the Python in Excel trial. If you like this blog series, check out my self-paced certification program, Anaconda Certified: Data Analysis with Python in Excel. The Analysis ScenarioThis blog post will continue the hypothetical scenario covered in Part 3 and Part 4 of this series – analyzing the impact of promotion strategies. The following monthly sales data table is included in this blog post’s workbook: Fig 01 – Monthly Sales Data The table in Fig 01 illustrates a very common scenario in business analytics – analyzing... --- - Published: 2024-02-06 - Modified: 2025-07-06 - URL: https://10.2.107.56:8443/blog/visual-data-analysis-with-python-in-excel-using-histograms - Categories: Product Many types of professionals analyze data using different skills. Microsoft Excel users analyze data using Excel PivotTables, while data scientists use statistical and machine learning models. Despite this diversity, Excel users, data scientists, and statisticians share one universal data analysis skill: visually analyzing data. This is the first in a five-part blog series introducing you to visually analyzing data with Python in Microsoft Excel. By the end of the blog series, you will have built fundamental skills in visually analyzing numeric, categorical, and time-series data. These skills are valuable to any professional, regardless of role or industry. Each post in the series has an accompanying Microsoft Excel workbook to download and use to build your skills. This post’s workbook is available for download here. For convenience, here are the links to all the blog posts in this series:Part 1 – Using Histograms (this post)Part 2 – Using Box PlotsPart 3 – Using Scatter PlotsPart 4 – Using Bar ChartsPart 5 – Using Line ChartsThere are a few things to note about this blog series: First, if you are new to Python in Excel, you should start with my Python for Excel Analysts blog series as it covers many concepts that are assumed in this blog series. Second, this series assumes you have enabled the Python in Excel public preview. This Microsoft article provides you with the information needed to get access to Python in Excel. Third, the blog series will use the Microsoft Excel Labs Python Editor for writing code.... --- --- ## News - Published: 2025-07-30 - Modified: 2025-07-31 - URL: https://stage.anaconda.com/newsroom/anaconda-raises-150m-series-c-funding-ai-enterprise With Insight Partners-led Round, Anaconda Establishes Role as the Standardized Python Distribution for Mission-Critical AI Systems AUSTIN, TX - – Anaconda, Inc. , the company committed to advancing AI with open source at scale, today announced that it raised over $150M in a Series C funding round led by Insight Partners, with participation from Mubadala Capital. The company operates profitably with over $150M in annual recurring revenue (ARR) as of July 2025. This news comes on the heels of Anaconda’s newly launched AI Platform as well as a recently announced partnership with Databricks, the data and AI company. Since its founding in 2012, Anaconda has been one of the most trusted and widely used Python distribution platforms, with over 21 billion downloads and 50 million users. Today, 95% of Fortune 500 companies and more than 10,000 large enterprises rely on Anaconda to build and manage AI systems effectively. The infusion of capital comes at a pivotal moment as enterprises shift from isolated data science projects to building compound AI applications, validating Anaconda’s mission to empower organizations and builders to innovate with data through a unified open source ecosystem for enterprise Python—the coding language that has become synonymous with AI development. “As agents and compound AI systems gain traction, companies need a foundational platform to effectively manage key open source artifacts and components to drive fast, scalable innovation. Anaconda takes this a step further by layering simplicity and security to AI in enterprise landscapes,” says George Mathew, Insight Partners Managing Director.... --- - Published: 2025-07-09 - Modified: 2025-07-22 - URL: https://10.2.107.56:8443/press/announcing-strategic-enhancement-to-conda-build Empowering builders and enterprises with 3-5x faster package creation while strengthening the entire open source ecosystemAUSTIN, TX (July 9, 2025) — Anaconda Inc. , the leader in advancing AI with open source, today announced a strategic initiative to enhance conda-build, incorporating performance innovations from Prefix. dev’s rattler-build. This collaboration will deliver significant performance improvements for conda package creation while maintaining the trusted conda-build experience that enterprises rely on. “Introducing this next generation of conda-build reaffirms our commitment to continuous innovation in the conda and greater open source ecosystem,” said Peter Wang, Co-founder and Chief AI and Innovation Officer of Anaconda. “By incorporating proven performance advances from rattler-build, we’re ensuring that conda-build evolves with every challenge, delivering unmatched power and efficiency as the premier solution for building and distributing packages in data science and AI development. ”The enhanced conda-build will leverage Rust-based technology from rattler-build to enable faster package building while ensuring compatibility with existing conda environments and workflows. Initial performance benchmarks show package-building speed improvements of 3-5x, dramatically reducing development time for organizations building custom environments and packages. As the integration supports both the current recipe format and the new standardized format, package maintainers benefit from a smooth migration with ensured compatibility across the ecosystem, simplifying workflows for contributors to both Anaconda and community channels. “The collaboration between Anaconda and Prefix. dev to bring rattler-build’s innovations to the broader conda ecosystem through conda-build will be a game-changer for the entire conda community,” said Wolf Vollprecht, CEO of Prefix. dev. “We’re... --- - Published: 2025-06-09 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/press/anaconda-partners-with-databricks-bridge-security-and-governance-gaps-enterprise-ai-development - News Category: Company Open Source Champions Join Forces to Accelerate Enterprise AI Adoption with Streamlined Data Science Workflows and Enhanced Security and ComplianceAUSTIN, TX (June 9, 2025) — Anaconda Inc. , the leader in advancing AI with open source, today announced a go-to-market partnership and native integration with Databricks, the Data and AI company. By combining Anaconda’s AI Platform delivering secure, curated open source Python packages with the scale, performance, and governance of the Databricks Data Intelligence Platform – enterprises will experience a new standard of enterprise-ready AI development with enhanced governance. Today’s enterprises invest millions in AI adoption, but they still struggle to deploy successfully due to three critical challenges: dependency blindspots creating a productivity imperative, crippling vulnerabilities expanding the risk surface, and a lack of visibility and confidence in delivering production-ready results. IDC Research predicts that 80% of AI project failures this year will result from these obstacles. Organizations need stability, consistency, and control to build AI systems at scale, not just experimentation environments alone. The availability of Anaconda’s AI Platform delivering an enterprise-grade Python ecosystem natively within Databricks Runtime brings together the best of open source and enterprise-grade security into one seamless experience, enabling developers to deploy and scale AI/ML applications confidently with reduced friction, accelerated time-to-value, and improved governance and compliance. ”This partnership addresses one of the biggest challenges of AI production deployment today: combining the speed of open source experimentation with the rigor of enterprise security, governance, and compliance requirements,” said Stephen Orban, SVP of Product Ecosystem and... --- - Published: 2025-05-13 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/introducing-the-anaconda-ai-platform - News Category: Company A Total Economic Impact Study Reveals Organizations Building AI with Anaconda Experience 80% Improvement in Operational Efficiency and 60% Reduced Risk of Security Breaches from Addressable Attacks AUSTIN, TX (May 13, 2025) — Anaconda Inc. , the leader in advancing AI with open source, today announced the release of the Anaconda AI Platform, the only unified AI platform for open source that centralizes everything you need to source, secure, build and deploy AI in an open source ecosystem. Recently recognized as an Enterprise Leader in G2’s Spring 2025 Report for Data Science and Machine Learning, the Anaconda AI Platform provides proven security and governance when leveraging open source for AI development, empowering enterprises to build reliable, innovative AI systems without sacrificing speed, value, or flexibility. As the only AI platform for open source, the Anaconda AI Platform combines trusted distribution, simplified workflows, real-time insights, and governance controls in one place to deliver secure and production-ready enterprise Python. Given Python’s prevalence as the language of choice for AI programming, this synergy gives users an unparalleled advantage, resulting in greater productivity, less risk, and a clearer path to value. Many organizations are turning to open source solutions, with 50% of data science practitioners leveraging open source and 66% of IT administrators stating open-source software is utilized at their companies, to speed up AI initiatives and build more flexible technology ecosystems. However, managing open source packages requires careful consideration for security, efficiency, and compliance challenges. The Anaconda AI Platform empowers organizations to leverage... --- - Published: 2025-03-18 - Modified: 2025-07-22 - URL: https://10.2.107.56:8443/press/anaconda-named-to-fast-company-most-innovative-companies-list Recognition validates pervasiveness of Python as foundational to advancement of AI as enterprise adoption soarsAUSTIN, TX (March 18, 2025) — Anaconda Inc. , the leader in advancing AI with open source, today announced that it has been named to Fast Company’s list of the World’s Most Innovative Companies of 2025. Anaconda was recognized in the Data Science category of the exclusive list, which spotlights businesses that are shaping industry and culture through their innovations to set new standards and achieve remarkable milestones in all sectors of the economy. “Anaconda is deeply committed to helping users drive value and fuel innovation through open source AI,” said Peter Wang, Chief AI and Innovation Officer and Co-founder. “Our recognition as one of the World’s Most Innovative Companies is a testament to Anaconda’s continued commitment to helping users simplify, safeguard, and accelerate data science and AI initiatives with open source at scale. By making our tools accessible for users across all proficiency levels, we’re helping enterprises improve customer experiences, reduce operational costs, and create new revenue streams. ” This prestigious recognition speaks to Anaconda’s critical role in pushing the boundaries of data science and AI with open source, equipping users to solve complex problems in a rapidly evolving tech landscape. Today, over 93% of Fortune 500 companies and more than 10,000 large enterprises rely on Anaconda, including Meta, ExxonMobil, European Commission, and Citibank. In 2024 alone, user adoption surged as organizations using Anaconda quadrupled to over one million. To meet demand, Anaconda announced the... --- - Published: 2025-01-21 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/press/ai-shortfalls-and-security-risks-demand-open-source-collaboration-anaconda-finds-in-state-of-data-science-report - News Category: Industry Seventh annual survey of data science professionals shows 87% are using AI as much or more than last year, but 43% feel unprepared for its challenges Anaconda Inc. , a leading provider for data science, machine learning, and AI, today released the 7th Annual Data Science Report: AI and Open Source at Work. Based on a survey of more than 3,000 data science professionals, IT workers, students, researchers, and professors, the report offers insight into trends and use cases across the data science, AI, and open-source communities. Anaconda’s analysis shows that AI and open-source tools have grown dramatically across industries. While fewer respondents are worried about losing their jobs to AI, other challenges have surfaced, including security concerns and a lack of readiness for AI’s growth. These shortfalls demand collaborative problem-solving, both within organizations and across the data science industry, via platforms that prioritize security, scalability, and ease of use. Key themes from the survey include: The AI readiness gap: While 87% of data science practitioners are using AI as much or more than they were last year, 43% feel unprepared for challenges like new AI tools and regulations, highlighting the need for secure, compliant AI deployment Collaboration is key: 66% of IT administrators report their companies are leveraging open-source tools, while half of data science practitioners say they actively incorporate open source into their workflows. Practitioners are using open-source software to build new tools (58%) and models for internal use (56%). AI as a tool, not a threat: Just... --- > Anaconda Business is now available in the AWS Marketplace, equipping users with a robust ecosystem of secure open-source tools and libraries. - Published: 2024-12-03 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/newsroom/anaconda-and-aws-unlock-new-possibilities-for-enterprise-ai-development - News Category: Company Austin, TX – December 3, 2024 – Anaconda Inc. , a leading provider for data science, machine learning, and AI, today announced that Anaconda Business is now available in the AWS Marketplace. Anaconda Business is a single platform that enables data science and AI teams to innovate and deploy solutions faster. Its inclusion in the AWS Marketplace will help to simplify AI model development by leveraging Anaconda’s robust ecosystem of open-source tools and libraries, while also addressing critical enterprise concerns around open-source software supply chain security and governance to foster AI innovation. Anaconda Business equips organizations with trusted tools and solutions to manage and scale data science projects in the cloud. Its inclusion in the AWS Marketplace provides: Comprehensive Open-Source Tools: Access over 200 trusted open-source data science and AI packages, including industry favorites like NumPy, pandas, and TensorFlow, ensuring faster data analysis, and AI model development and training Enhanced Security and Governance: Ensure secure and compliant operations through the Anaconda Package Security Manager and features such as security vulnerability monitoring, signature verification, policy filters, and a detailed software bill of materials (SBOM). Seamless Scalability: Combine AWS’s infrastructure with Anaconda Business to effortlessly manage large datasets and complex computations, scaling projects without limits Streamlined Deployment: Quickly deploy secure Anaconda environments within AWS with just a few clicks, saving time and reducing operational complexity “Anaconda Business in the AWS Marketplace enables organizations to build, scale, and secure their data science operations like never before,” said Peter Wang, Chief AI and Innovation... --- > Anaconda releases Anaconda Toolbox into general availability, equipping Excel users with Python-powered data analysis and collaboration tools. - Published: 2024-11-19 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-toolbox-general-availability - News Category: Company Anaconda Toolbox Enters General Availability at Microsoft Ignite with Enhanced Features Driven by User Feedback to Run Python in Excel Locally AUSTIN, TX – November 19, 2024 – Anaconda Inc. , a leading provider for data science, machine learning, and AI, today announced the general availability of Anaconda Toolbox for Excel, a new add-in for Microsoft Excel that seamlessly brings Python's robust capabilities to the familiar spreadsheet interface. This release empowers Excel users across all teams to leverage Python for data analysis, visualization, and automation regardless of their Python experience level. As the use of Python in Excel has rapidly grown, Anaconda has responded to community feedback with a robust set of features that meet the real-world needs of data professionals. With secure, accessible analytics tools — including enhanced data preparation, statistical modeling, and visualization features — the toolbox guides beginners while enabling developers to run complex code and support data driven decision-making. Additionally, the toolbox enhances collaboration by enabling seamless data- and code-sharing and joint analysis between Python and Excel users, bridging skills gaps within teams. Beyond its user-driven enhancements, the Anaconda Toolbox includes Anaconda Code, which allows users to run Python code locally in Excel and, for the first time, create custom user-defined functions (UDFs). These functions can be decorated and called directly from the Excel grid, enabling developers to share tools with non-Python users who have complex analytical needs. Users can also import projects from PyScript, simplifying the creation and distribution of complex classes and custom functions... --- > Anaconda's AI Navigator is now generally available to all users. Build AI agents with the latest generative AI models on your desktop. - Published: 2024-10-01 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-ai-navigator-generative-ai-desktop-agent - News Category: Company The desktop application allows to securely access and run over 200 pre-trained Generative AI models locally The free desktop application allows users of all levels to securely access and run over 200 pre-trained Generative AI models locally Austin, TX— — Anaconda Inc. , the dominant platform for AI, data science and machine learning, today announced the general release of AI Navigator, a free and powerful desktop application that brings the capabilities of large language models (LLMs) directly to users’ desktops. Users can now interact with locally running LLMs without sending any private information off of their Windows or Mac devices to external cloud services and infrastructure providers. As AI technologies continue to evolve rapidly, so have the challenges of securely adopting and managing these tools. Anaconda's AI Navigator, which is available for free download, addresses these challenges by offering access to a curated selection of over 200 AI models, tailored for a variety of tasks and device capabilities. Early user interest has been especially strong for both code generation and debugging, with five of the top ten most downloaded models during the public beta coming from the CodeGemma and CodeLlama models. With over 10,000 users actively conducting chats and downloading models, the insights gleaned have already led to significant improvements, including a 300% increase in platform launch speeds. Designed with both individuals and enterprises in mind, the tool offers a secure, efficient, and user-friendly way to explore and run generative AI models on your desktop or laptop. Users can access... --- - Published: 2024-09-16 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-microsoft-python-in-excel-general-availability - News Category: Company Following a successful Public Preview, Python in Excel makes secure Python-powered data analysis and machine learning possible. AUSTIN, TX — September 16, 2024 — Anaconda Inc. , a leading provider for data science, machine learning, and AI, today announced the general availability of Python in Excel. This integration with Microsoft Excel allows users to run Python code securely and directly within Excel's grid, requiring no separate Python installation. Since released into Public Preview in August 2023, Python in Excel has seen widespread applause from both the data science community and business analysts alike. Microsoft Insiders have started using Python in Excel to perform complex analyses without leaving their familiar Excel environment. Now released to the general public, Python in Excel is set to transform the way millions of Excel users and Python practitioners approach their work. Excel users can now use Python's advanced capabilities for data manipulation, statistical analysis, and data visualization without leaving their familiar spreadsheet environment. This opens up new possibilities for complex analyses and sophisticated visualizations. Python practitioners can also seamlessly blend their scripts and rich visualizations with the widespread accessibility of Excel. This integration ensures an uninterrupted workflow and simplifies sharing their work with colleagues who primarily use Excel. "We are thrilled to announce the general availability of Python in Excel, a major breakthrough that will transform the workflow of millions of Excel users around the world," said Peter Wang, Co-Founder and Chief AI & Innovation Officer at Anaconda. "This integration accelerates the democratization of Python... --- - Published: 2024-09-16 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-microsoft-python-in-excel-general-availability - News Category: Company Following a successful Public Preview, Python in Excel makes secure Python-powered data analysis and machine learning possible. AUSTIN, TX — September 16, 2024 — Anaconda Inc. , a leading provider for data science, machine learning, and AI, today announced the general availability of Python in Excel. This integration with Microsoft Excel allows users to run Python code securely and directly within Excel’s grid, requiring no separate Python installation. Since released into Public Preview in August 2023, Python in Excel has seen widespread applause from both the data science community and business analysts alike. Microsoft Insiders have started using Python in Excel to perform complex analyses without leaving their familiar Excel environment. Now released to the general public, Python in Excel is set to transform the way millions of Excel users and Python practitioners approach their work. Excel users can now use Python’s advanced capabilities for data manipulation, statistical analysis, and data visualization without leaving their familiar spreadsheet environment. This opens up new possibilities for complex analyses and sophisticated visualizations. Python practitioners can also seamlessly blend their scripts and rich visualizations with the widespread accessibility of Excel. This integration ensures an uninterrupted workflow and simplifies sharing their work with colleagues who primarily use Excel. “We are thrilled to announce the general availability of Python in Excel, a major breakthrough that will transform the workflow of millions of Excel users around the world,” said Peter Wang, Co-Founder and Chief AI & Innovation Officer at Anaconda. “This integration accelerates the democratization of Python... --- > The new Anaconda Code via the Anaconda Toolbox for Excel allows users to run Python code locally within their workbooks. - Published: 2024-07-31 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/newsroom/anaconda-code-toolbox-python-in-excel - News Category: Company Data science and AI platform provider’s Anaconda Toolbox helps users conduct Python-powered projects in Excel locally with its Anaconda Code add-in Data science and AI platform provider's Anaconda Toolbox helps users conduct Python-powered projects in Excel locally with its Anaconda Code add-in Austin, Texas - : Anaconda Inc. a leading provider for data science, machine learning, and AI, today announced the public beta release of Anaconda Code within its Anaconda Toolbox for Excel. Anaconda Code empowers users to write Python code directly within Excel and run it locally. By running code locally, it provides users flexibility and control over their Python environments, eliminating the need to wait for network communication and keeping all code and data within the workbook. Since its introduction in August 2023, Python in Excel has enabled users to perform data manipulation, analysis, and visualization, as well as advanced machine learning and AI tasks, directly within Excel spreadsheets. Until now, the Python code would run on Microsoft Azure’s secure cloud servers. With Anaconda Code, Excel users gain the exclusive ability to run Python code on their local machines without relying on external compute services. Excel users have long valued security, shareability, and long-term reproducibility in their spreadsheets. Anaconda Code addresses these challenges through its WebAssembly-based technology that enables local, secure Python execution without requiring separate installations or complex environment management. By bridging the gap between traditional spreadsheet use and advanced coding practices, this solution grants users access to a wider Python ecosystem, enhancing data analysis capabilities while maintaining Excel's core strengths. "With Anaconda Code, we're giving users freedom to control the environment," said Peter Wang, Co-Founder and Chief... --- > Anaconda integrates its secure repository of Python and R packages into Snowflake Notebooks to accelerate data science and AI projects. - Published: 2024-07-09 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-snowflake-notebooks-integration - News Category: Company Data science and AI platform provider delivers secure Python Packages for Snowflake users, furthering its mission to make technology accessible to all. Austin, Texas - July 9, 2024 — Anaconda Inc. , a leading platform provider for data science, machine learning, and AI, today announced a new integration with Snowflake Notebooks (public preview), a cell-based development interface integrated within Snowflake’s secure, scalable platform. Snowflake Notebooks provide a convenient, easy-to-use development interface for Python, SQL, and Markdown to accelerate development using Snowflake offerings, including Snowflake ML, Streamlit, and Snowflake Cortex AI. The integration brings Anaconda's secure, efficient, and robust Python packages within Snowflake Notebooks directly to accelerate data science, machine learning and AI development. This integration extends Anaconda deeper into the Snowflake AI Data Cloud, empowering users to keep their data and development workflows within Snowflake's secure and scalable platform. Leveraging Anaconda’s curated package repository enables users to meet stringent security standards and manage package dependencies effortlessly. This integration helps data scientists to focus on building and deploying models, without the typical security concerns associated with open-source software. “The integration of Snowflake Notebooks with Anaconda represents a significant step forward in our mission to democratize Python and enable users to perform data science and AI tasks more efficiently,” said Peter Wang, Chief AI & Innovation Officer, Anaconda. “By combining the robust capabilities of Anaconda with the innovative features of Snowflake Notebooks, we are empowering users to drive faster insights, streamline workflows, and unlock new opportunities for collaboration and innovation in their... --- > Integrating Anaconda's secure Python and R packages with Teradata's ClearScape Analytics aims to accelerate data science and generative AI applications. - Published: 2024-04-09 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-teradata-partner-open-source-ai - News Category: Company Integrating Anaconda’s secure repository of Python and R packages with Teradata’s powerful ClearScape Analytics is intended to speed delivery and use of data science, generative AI use cases AUSTIN, TX and SAN DIEGO – April 9, 2024 – Anaconda Inc. and Teradata today announced a new integration to bring the most popular and widely used Python and R packages to Teradata VantageCloud through the Anaconda Repository. The integration with ClearScape Analytics, a powerful engine for deploying end-to-end artificial intelligence (AI) and machine learning (ML), is designed to provide enterprises with the ability to deploy large-scale data science, AI/ML, and generative AI use cases that can cost-effectively deliver value for the enterprise. Organizations working to leverage AI innovation need a platform that allows for the quick application of popular and secure open-source packages, but that also delivers scale, performance, and access to harmonized data and trusted AI. Anaconda and Teradata believe their partnership meets this critical need by speeding the deployment and operationalization of AI/ML developed using Anaconda’s secure repository of open-source Python and R packages. “There is so much innovation happening in the open-source community, and we’re thrilled to be working with Anaconda to bring their popular open-source packages to Teradata VantageCloud Lake,” said Hillary Ashton, Chief Product Officer at Teradata. “We believe that the 45 million data scientists, data engineers, developers and analytics professionals that use Anaconda will have an even greater impact on their organizations by also using ClearScape Analytics to deploy and operationalize trusted AI/ML at enterprise... --- > IBM watsonx.ai users can access Anaconda’s open-source software repository and have the option to upgrade to the premium repository. - Published: 2024-02-13 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-partners-with-ibm-watsonx-to-deliver-enterprise-scale-ai-solutions - News Category: Company AUSTIN, TX - February 13, 2024 - Anaconda Inc. , provider of the world’s most popular platform for data science and modern AI development, today announced an expanded collaboration with IBM. As part of this expansion, IBM watsonx. ai users can access Anaconda’s natively built open-source Python repository, and watsonx. ai can integrate with Anaconda Repository on-premises for Python security vulnerability management and license management. Through this collaboration, IBM watsonx. ai users gain access to Anaconda’s base open-source software (OSS) repository as well as the option to upgrade to Anaconda’s premium repository with advanced security features for the enterprise. Anaconda empowers users to access the open-source Python libraries at the heart of modern AI development. Whether it’s generative AI applications or advanced statistical modeling, with Anaconda’s secure repository, both individuals and large enterprises can build, test, and deploy AI innovations at scale. With Anaconda and IBM watsonx. ai, users can: Build AI applications choosing from the latest OSS Python packages and AI frameworks Guide watsonx. ai foundation models to address your needs, with tools for building and refining performant prompts Tune the enterprise AI software supply chain with the Anaconda Repository, which provides additional security with vulnerability analysis, channel management, and open-source security best practices “Open-source Python and its rich ecosystem have become essential foundations for modern AI and that’s why it’s never been more important to find partners you can trust,” said Anaconda CEO Barry Libert. “This collaboration with IBM will help address many of the challenges surrounding deploying... --- > AI Alliance established two pivotal working groups focused on AI Safety and Trust Tooling and AI Policy Advocacy. Anaconda participates in both groups - Published: 2024-02-08 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-joins-ai-alliance - News Category: Company February 8, 2024 – Anaconda Inc. , the leading provider of the world’s most popular source code for AI, machine learning, and data science, today announced its membership in the AI Alliance, an international community of developers, researchers, and organizations dedicated to promoting open, safe, and responsible artificial intelligence (AI). Joined by industry leaders like IBM, Meta, Oracle, and Snowflake, Anaconda is committed to collaboratively advancing AI innovation while ensuring its accessibility and safety. In its mission to promote the ethical development and deployment of AI, the AI Alliance has established two pivotal working groups focused on AI Safety and Trust Tooling and AI Policy Advocacy. Anaconda will actively participate in both groups, leveraging its vast experience and resources to contribute to the development of: Objective resources and guidance on AI safety, trust, ethics, and cybersecurity. Enhanced tools and methodologies for evaluating AI models and datasets, particularly in areas of sensitive data detection, model quality, and cybersecurity. Benchmarking standards for AI model and application testing, ensuring robust and reliable AI solutions. A forum for open dialogue between the technical community and policymakers, aimed at fostering open innovation in AI. Influential insights and opinions on critical AI policy issues, representing the open-source AI ecosystem in policy discussions. "This journey is more than AI—it's the shared pursuit for a future guided by scientific curiosity, relentless innovation, and boundless access in which technology uplifts businesses and society to the highest standards of integrity and responsibility,” said Anaconda CEO Barry Libert. “Anaconda is fully... --- > Anaconda seamlessly integrates Python into Microsoft Excel's grid to extend data science, artificial intelligence, and machine learning capabilities to all Excel users. - Published: 2024-01-30 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-leadership-ai-incubator - News Category: Company AUSTIN, TX – January 30, 2024 -  Anaconda Inc. , the leading provider of the world’s most popular source code for AI, machine learning, and data science, today announced a new AI Incubator and changes to its executive leadership team. Peter Wang was named Chief AI & Innovation Officer and will lead Anaconda’s new AI Incubator. The AI Incubator will serve as an internal research and development group dedicated to advancing Python performance in AI workloads and supporting the company’s competitive advantage. In line with Anaconda’s mission to empower the world with the power of AI, data science, and Python, Anaconda’s AI Incubator will work to develop new breakthroughs in open-source Python and AI that will enable the next generation of AI applications and products. More details on Anaconda’s AI Incubator will be released over the coming months. Wang will also continue to guide Anaconda’s growth as a member of its board of directors. Barry Libert was named Anaconda’s CEO, after serving on Anaconda’s board of directors for the past four years. As CEO, he will focus on accelerating the Company’s growth as well as the size and scope of its user and developer community. As a serial entrepreneur, Barry comes with over 30 years of board and operating experience focused on creating exponential value through network effects and data in multiple tech sectors. Alongside Wang’s and Libert’s new roles, the following executive leadership changes will take effect immediately: Jessica Reeves will remain Chief Operating Officer with expanded responsibilities focused... --- > Anaconda's 2023 State of Data Science report highlights how generative AI creates new talent needs, fears, and opportunities in the field. - Published: 2023-09-26 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-state-of-data-science-2023 - News Category: Company Generative artificial intelligence (AI) triggers new talent requirements, fears, and opportunities for the future of data science. AUSTIN, Texas — September 26, 2023 — Anaconda Inc. , provider of the world’s most popular platform for data science and modern AI development, released its sixth annual State of Data Science report, surfacing insights into today’s vibrant data science community and the growth and usage of AI and open-source software. Unlike previous years, the 2023 report delves into the ways generative artificial intelligence (AI) is reshaping the industry’s trajectory and fundamentally changing the way data scientists work. The global study targeted four cohorts across the open-source and data science community: IT professionals, data science practitioners, academics, and students. In just a few years, AI has become integrated into the fabric of the business world, but in 2023, we see rapid adoption and innovation, especially in the burgeoning interest in generative AI. Among data science respondents in this year's report, 68% confirmed that their company is building new products with AI technologies, and 40% are working specifically on internal generative AI tools such as large language models (LLMs). The shift in focus presents unique opportunities and challenges compared to previous years. As the technological frontier changes, several unmistakable themes have emerged: the competitive race toward generative AI is necessitating new talent requirements; the misalignment on security between IT and data science teams has reached a tipping point; and data practitioners are increasingly confident that they are delivering measurable business value. The Pace of... --- > Anaconda has integrated Python into Microsoft Excel’s grid to extend data science, artificial intelligence, and machine learning capabilities to Excel users. - Published: 2023-08-22 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-distribution-for-python-brings-data-science-to-hundreds-of-millions-of-microsoft-excel-users - News Category: Company Anaconda seamlessly integrates Python into Microsoft Excel's grid to extend data science, artificial intelligence, and machine learning capabilities to all Excel users. August 22, 2023 — Anaconda Inc. , the provider of one of the world's most widely used and trusted data science and AI platforms, today announced the beta availability of Anaconda Distribution for Python in Excel, a new integration with Microsoft Excel. Anaconda's Python distribution is fully embedded and integrated into the Excel grid toolboxes for manipulating, analyzing, and visualizing data and for advanced machine learning and AI. Python in Excel is currently rolling out to Public Preview and is available for Microsoft Insiders. This integration comes at a time of surging demand for both sophisticated and accessible tools for data analysis, modeling, prediction, and AI. Data science has been transforming businesses for over a decade, but the complexity of its tools has limited its reach outside of technical experts. Data scientists must frequently wrestle with command-line environments and manage complex software configurations. The integration of Anaconda-curated Python libraries directly into the Excel grid radically simplifies the analysis workflow and is a paradigm shift in democratizing data science. The fundamental promise of business analytics tools is to facilitate better data-driven decision making in the enterprise. Over the last decade, Python-based data science has transformed every enterprise driven by the explosion in popularity of the Python programming language. Now, the combined power of the rich Python ecosystem with the ubiquitous datasets and models that live within Excel will unlock... --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2023-08-08 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/newsroom/anaconda-supports-scalable-open-source-analysis-sosa-stack-for-high-powered-scientific-data-analysis The Pandata stack is a groundbreaking set of general-purpose and powerful open-source data analytics tools offering fully scalable data processing for any domain. AUSTIN, TX – August 8, 2023 – Anaconda Inc. , provider of the world’s most popular data science & AI platform, today announced its support of the Pandata stack, providing freely available open-source data analytics tools for processing data of any size and for any domain. The collection of tools includes high-performance, cloud-friendly, OS-independent Python libraries offering data analysis, visualization, and processing at scale, and across all areas of research, development, and operations. Python has become the most popular programming language in the world, partially due to the wide range of open-source libraries available that cover almost any area of science, engineering, and data analysis. However, many available tools are highly specialized and restricted to addressing small problems in confined domains. Pandata’s stack of general-purpose, interoperable, and compositional tools includes Dask, Xarray, Numba, hvPlot, Jupyter, and more, providing a versatile and sustainable shared platform for data analysis and scientific computation. Collectively, Pandata covers the landscape of data access, distributed computation, and interactive visualization across any domain or scale, letting researchers and practitioners in each field focus on the much smaller set of code that is required for their own specific domain. As stewards of the open-source Python ecosystem, Anaconda has invested heavily into the creation of many of the tools within the Pandata stack, including Dask, Bokeh, and Panel. Additionally, Anaconda staff contribute extensively to other tools... --- > The first-ever conda certification program aims to help learners unlock the ability to package and distribute software. - Published: 2023-07-05 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-launches-conda-fundamentals-certification The first-ever conda certification program aims to help learners unlock the ability to package and distribute software. AUSTIN, TX – July 5, 2023 – Anaconda Inc. , provider of the world’s most popular data science platform, today announced the launch of its conda Fundamentals certification program. In partnership with NumFOCUS, conda Fundamentals is the first-ever certification program offered by Anaconda and the only conda certification program currently on the market. By completing the conda Fundamentals certification, learners will understand how to employ conda’s power to effortlessly manage software; gain experience deploying, building, and debugging software; and confidently begin their journey in software engineering or data science. This certification is offered in partnership with NumFOCUS, and a portion of the proceeds will be donated to NumFOCUS to support the conda open-source project. The certification program furthers Anaconda’s presence within the education field. Over the past year, Anaconda has launched several initiatives to reinforce its commitment to supporting individuals throughout their data science journey with PyScript, Anaconda Learning, Anaconda Notebooks, and Data Science Expos. In May, the company also acquired EduBlocks, a free, web-based, drag-and-drop coding platform built to help K-12 students learn fundamental skills, expanding Anaconda’s core SaaS offerings for Python development. “As AI becomes more ingrained in our lives with the introduction of mainstream generative AI, the shortage of skilled talent in this area is creating uncertainty for both employees and employers,“ said Jessica Reeves, COO, Anaconda. “Creating accessible learning tools for students and professionals to upskill and reskill to meet... --- > \Anaconda expands its core SaaS offerings to provide accessible, cloud-based resources for Python development at all skill levels. - Published: 2023-05-04 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-acquires-edublocks-to-empower-k-12-data-literacy-and-expand-educational-offerings - News Category: Company With the latest acquisition of EduBlocks, Anaconda expands its core SaaS offerings to provide accessible, cloud-based resources for Python development at all skill levels. AUSTIN, TX – May 4, 2023 -  Anaconda Inc. , provider of the world’s most popular data science platform, today announced the acquisition of EduBlocks, a free, web-based, drag-and-drop coding platform built to help K-12 students learn fundamental skills. With EduBlocks, Anaconda expands its reach and offerings for K-12 schools as well as for beginner-level professionals. By acquiring EduBlocks, Anaconda will expand its SaaS offerings, which already includes Anaconda Learning, Anaconda Notebooks, and PyScript. com. As coding becomes easier and more accessible, tools like EduBlocks will be instrumental in helping users of all backgrounds learn to code quickly. Trusted by over 35 million users, educators and school IT staff who use Anaconda now have access to a dedicated partner committed to keeping students safe while teaching the skills of tomorrow. "EduBlocks is a perfect fit for Anaconda as it aligns closely with our mission of empowering the world with data literacy,” said Jessica Reeves, COO of Anaconda. "Data literacy and Python are some of the most in-demand job skills and with this acquisition Anaconda will be able to support learners at every level, from students in elementary school to professionals learning new skills for the first time. EduBlocks is an invaluable tool and we’re excited to bring its capabilities into Anaconda’s ecosystem. ” Based in the United Kingdom and founded in 2016, EduBlocks has 100,000 users... --- - Published: 2023-04-19 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-debuts-data-science-expo-to-encourage-student-data-literacy The competition-based expo will host three regional events in 2023 designed to provide a comprehensive and interactive learning experience AUSTIN, TX – April 19, 2023 – Anaconda Inc. , provider of the world’s most popular data science platform, today unveiled its first-ever Data Science Expo taking place in Austin, Texas on April 22, 2023. This event is the first in a series to engage high school and college-level students in building foundational data science and Python skills, regardless of prior experience. The data science industry has boomed over the last decade as the need for data-driven decision-making has grown—boosting data scientist to #3 on Glassdoor’s 50 Best Jobs in America for 2022. As the industry thrives and the demand for talent continues to grow, there is an opportunity to upskill the future workforce and remove existing barriers to entry for those curious about the world of coding. True to Anaconda’s overall mission to democratize data science and Python development, the multi-city expo and competition reinforces the company’s desire to invest in younger generations and close the digital divide by providing equal access to the tools and resources students need to build data and Python skills. “As we move further into a world dominated by data, AI, and machine learning, it’s critical to provide opportunities for younger generations to keep up with the skills needed to enter the workforce and meet modern data literacy requirements,” said Jessica Reeves, COO, Anaconda. “Nontraditional pathways into data science like expos, mentorship programs, and scholarships can... --- > AUSTIN, TX – April 4, 2023 – Anaconda Inc., provider of the world’s most popular platform to develop and deploy secure Python solutions, today announced a new channel partner program for Value-Added Resellers (VARs), Distributors, Direct Market Resellers (DMRs), Global System Integrators (GSIs), and Referral Partners worldwide to accelerate Python and open-source package deployments to the enterprise. - Published: 2023-04-04 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-launches-new-channel-partner-program - News Category: Company AUSTIN, TX – April 4, 2023 – Anaconda Inc., provider of the world’s most popular platform to develop and deploy secure Python solutions, today announced a new channel partner program for Value-Added Resellers (VARs), Distributors, Direct Market Resellers (DMRs), Global System Integrators (GSIs), and Referral Partners worldwide to accelerate Python and open-source package deployments to the enterprise. The new program will advance existing global reselling activities while introducing new incentives, training capabilities, and certifications to strengthen the partner ecosystem. AUSTIN, TX – April 4, 2023 – Anaconda Inc. , provider of the world’s most popular platform to develop and deploy secure Python solutions, today announced a new channel partner program for Value-Added Resellers (VARs), Distributors, Direct Market Resellers (DMRs), Global System Integrators (GSIs), and Referral Partners worldwide to accelerate Python and open-source package deployments to the enterprise. Anaconda’s secure repository of open-source Python packages is used by organizations worldwide to power the data science projects and artificial intelligence (AI) and machine learning (ML) models driving innovation. As trusted technology advisors to some of the most successful global enterprises, channel partners play a key role in evangelizing Anaconda and supporting the company’s growth. These channel partners will further benefit from Anaconda’s own expertise to manage and mitigate security risks. “Cultivating strong partnerships in the data science and Python community is pivotal to our company’s growth and trajectory,” said Peter Wang, CEO and co-founder of Anaconda. “Together, with our partners, we envision a world where Anaconda is in every enterprise environment that uses Python. Our partners’ success is our success, and the changes we’ve made to the channel and enablement program will help drive company-wide momentum this year and in years to come. ” With this new program, Anaconda’s channel program will offer an enhanced global onboarding experience that facilitates global expansion while providing world-class support to global reselling... --- - Published: 2023-03-24 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-launches-pyscriptcom-democratizes-python-for-all - News Category: Company The revolutionary platform enables programming for the 99%, advancing Anaconda’s mission to democratize data science and Python development. AUSTIN, TX – March 28, 2023 – Anaconda Inc. , provider of the world’s most popular data science platform and incubator of the open-source PyScript project, today unveiled PyScript. com, a free and flexible coding platform where anyone in the world can create next-generation web applications with Python-powered data interactivity and computation. The platform is now generally available for free as a software service. “PyScript. com wasn’t designed for just professional developers and data scientists, rather for the 99% of the world that aren’t Python proficient or professionals,” said Peter Wang, CEO and co-founder of Anaconda. “Today, Python is one of the top programming languages and one of the easiest to learn, yet we estimate that less than 1% of the world’s population knows how to code. If we’re dedicated to a digital-first future, we must break the barrier of entry to coding. PyScript. com removes the burden of installing dev tools and creates an easy-to-use experience for the 99% to play with code and build neat things they can easily share. ”After debuting the open-source PyScript project in April 2022 as a proof-of-concept, Anaconda has spent the last year improving the open-source project. Today Anaconda is announcing the launch of PyScript. com, a site that allows anyone to build rich, interactive, shareable Python-powered web applications directly in the browser. PyScript. com’s plug-and-play modular development environment can run on any browser, including... --- - Published: 2022-11-09 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-and-domino-data-lab-to-deliver-integrated-ai-ml-lifecycle-support-for-python-and-r-users - News Category: Company New partnership adds access to the complete Anaconda repository within Domino’s Enterprise MLOps platform for faster time-to-valueAUSTIN, Texas, November, 9 2022 – Anaconda Inc. , provider of the world’s most popular data science platform with over 30 million users, today announced a collaboration with Domino Data Lab, provider of the leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, to integrate Anaconda’s secure Open-Source Software (OSS) repository into the Domino platform. The partnership will allow researchers and data scientists using Python and R to create, use, and reproduce code, models, and insights with teams, easily and securely. Through this integration with the Anaconda Repository, Domino users gain seamless access to the complete and secure OSS Python/R package repository hosted, built, and maintained by Anaconda without the need for a separate enterprise license. Data science teams will achieve faster time-to-value through self-serve, instant access to the open-source Python and R tools used in every industry today. “We’ve built a secure repository that enables organizations to confidently scale open-source efforts, supporting over 30 million Anaconda users and the tools and platforms they depend on,” said Al Gashi, SVP, Worldwide Revenue, Anaconda. “This partnership with our friends at Domino Data Lab is incredibly exciting because it puts the power of open-source Python directly into the hands of data science teams changing the world. We believe our secure repository and the Domino Enterprise MLOps platform can provide customers a more complete platform that fosters innovation securely and with governance in place.... --- - Published: 2022-11-07 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-and-snowflake-announce-general-availability-of-snowpark-for-python-integration - News Category: Company The native integration brings Anaconda to Snowflake Data Cloud usersAUSTIN, TX – November 7, 2022 – Anaconda Inc. , provider of the world’s most popular data science platform, today announced that Snowpark for Python, which embeds Anaconda’s data and machine learning packages in Snowflake’s Data Cloud, has entered General Availability (GA). Snowpark users’ seamless access to Anaconda’s curated package repository helps address two of the biggest challenges data scientists face using open-source software: Meeting InfoSec standards and managing package dependencies in their computing environments. In Anaconda’s 2022 State of Data Science Report, respondents cited fear of vulnerabilities and risks in the software supply chain as the biggest roadblock to leveraging open-source software. Security and dependency management were cited as some of the biggest roadblocks when moving models to a production environment. “Since we announced the Public preview of Anaconda in Snowpark for Python this June, data scientists have told us that the ability to use their favorite programming language directly inside the database has been a game-changer,” said Peter Wang, CEO and co-founder, Anaconda. “Snowflake users can be more productive with cutting-edge machine learning tools, while meeting the needs of organizational governance; at the production end, it is easier for the business to ‘see’ machine learning models and deploy them into business environments. ”“As a major contributor to open source projects, Snowflake wanted to bring enterprise-grade open-source innovation to the Snowflake Data Cloud,” said Torsten Grabs, Director of Product Management, Snowflake. “By embedding Anaconda’s repository and package manager into the... --- - Published: 2022-09-14 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-research-finds-majority-of-organizations-scaled-back-their-open-source-software-usage-due-to-security-fears - News Category: Company Rising security concerns, limited talent, and ethical dilemmas are seen as the biggest threats to the future of data scienceAUSTIN, Texas — September, 14 2022 — Anaconda Inc. , provider of the world’s most popular data science platform, released its annual 2022 State of Data Science report, revealing the widespread trends, opportunities, and perceived blockers facing the data science, machine learning (ML), and artificial intelligence (AI) industries. The global study targeted the open-source community through three cohorts of academics, industry professionals, and students. While open-source software was created by and for developers, it is now an integral part of commercial software development and the backbone for continuous enterprise innovation. Of those surveyed, 20% identified open source’s speed of innovation and affordability as the most valued benefits of its usage. When asked about the biggest threats to further innovation and advancement within the open-source community, respondents focused on several areas:Concerns Around Open-Source Security Are GrowingOpen-source security continues to be top of mind, given incidents that have troubled the industry over the last year, including the Log4j breach and the rise of protestware. As a result, 40% of professional respondents indicated that their organizations scaled back their open-source software usage in the past year due to concerns around security. Additionally, 31% of professionals stated that “security vulnerabilities” were the biggest challenge in the open-source community today. While most organizations use open-source software, of the 8% of respondents whose organizations are not, 54% said the biggest reason is fear of potential vulnerabilities, exposures,... --- - Published: 2022-08-09 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-oracle-partnership - News Category: Company Anaconda and Oracle partner to help secure the open-source pipeline in high-performance machine learning on Oracle Cloud Infrastructure (OCI)AUSTIN, TX –August 9, 2022 – Anaconda Inc. , provider of the world’s most popular data science platform, today announced a collaboration with Oracle Cloud Infrastructure to offer secure open-source Python and R tools and packages by embedding and enabling Anaconda’s repository across OCI Artificial Intelligence and Machine Learning Services. Customers have access to Anaconda services directly from within OCI without a separate enterprise license. “We are committed to helping enterprises secure their open-source pipelines through the ability to use Anaconda anywhere, and that includes inside the Oracle Cloud,” said Peter Wang, CEO and co-founder of Anaconda. “By combining Anaconda’s package dependency manager and curated open-source repository with OCI’s products, data scientists and developers can seamlessly collaborate using the open-source Python tools they know and trust – while helping meet enterprise IT governance requirements. ”Python has become the most popular programming language in the data science ecosystem, and for good reason; it is a widely-accessible language that facilitates a wide variety of programming-driven tasks. Because the velocity of innovation powered by the open-source community outpaces any single technology vendor, more and more organizations are adopting open-source Python for enterprise use. “Oracle’s partnership to provide data scientists with seamless access to Anaconda not only delivers high-performance machine learning, but also helps ensure strong enterprise governance and security,” said Elad Ziklik, vice president, AI Services, Oracle. “With security built into the core OCI experience,... --- - Published: 2022-06-22 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-acquires-pythonanywhere - News Category: Company The acquisition adds capabilities designed to unite teams and create access to more robust cloud resources. AUSTIN, Texas–(BUSINESS WIRE)–Anaconda Inc. , provider of the world’s most popular data science platform, today announced that it has acquired PythonAnywhere, a cloud-based Python development and hosting environment. As a fully-fledged, cloud-based Python development environment, PythonAnywhere removes the heavy burden of infrastructure management and empowers Python developers to simply create web applications within a cloud-based Python environment—a critical component to collaborating and sharing within dispersed teams. PythonAnywhere’s added expertise allows Anaconda to more deeply support its user community of over 30 million individuals and ensure Python developers have access to a cloud-based environment with notebooks, tools, and a simple way to collaborate with their team. The acquisition comes on the heels of Anaconda’s release of PyScript, an open-source framework running Python applications within the HTML environment. The PythonAnywhere acquisition and the development of PyScript are central to Anaconda’s focus on democratizing Python and data science. “Today, non-programmers are the fastest-growing group of Python users. For Python to maintain this growth and remain the most widely used data science programming language in the world, it’s imperative to increase accessibility and remove barriers to collaboration,” said Peter Wang, CEO and co-founder of Anaconda. “With the PythonAnywhere acquisition, Anaconda will extend its services to all Python developers while building on capabilities for data scientists, engineers, data science enthusiasts, and students. This is an exciting milestone for our company, and we’re thrilled about the opportunity to strengthen the... --- - Published: 2022-06-14 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anacondas-embedded-repository-and-package-manager-in-snowpark-for-python-to-enter-public-preview - News Category: Company The native integration brings Anaconda, the world’s most popular data science platform, to Snowflake Data Cloud users seeking enterprise-grade Python workflows. AUSTIN, Texas –(BUSINESS WIRE)–Anaconda Inc. , provider of the world’s most popular data science platform, today announced that Snowpark for Python, an Anaconda-embedded repository and package manager for secure and seamless access to open source in Snowflake’s Data Cloud, will enter the public preview stage at Snowflake Summit in Las Vegas this week. Python’s rich ecosystem of open-source packages is one of the biggest enablers for data science. With Snowpark, Snowflake’s developer framework, Snowflake and Anaconda will allow data engineers, data scientists, and developers who prefer using Python as their programming language of choice to take advantage of Snowflake’s powerful platform capabilities and securely collaborate on a single platform. “Over the past few years, Python has experienced explosive demand and growth as it’s become one of the most popular programming languages,” said Peter Wang, CEO and co-founder, Anaconda. “It’s an unstoppable adoption engine, so it’s exciting to put Python on a very exclusive list of business tools that you can use right inside of Snowflake’s Data Cloud. This is a game-changer for the data science community. ”The integration puts the power of Anaconda in the hands of Snowflake Data Cloud users, who already benefit from Snowflake’s elasticity and performance. Users can now run secure Python-based workflows without the need to copy or move data, and access the most popular open-source Python packages by using Anaconda in Snowflake—without any manual... --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2022-04-12 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/newsroom/anaconda-announces-collaboration-with-esri-setting-the-enterprise-standard-for-python-across-the-geospatial-community - News Category: Company Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. Anaconda partners with Esri to provide users of ArcGIS, the world’s leading geographic information system (GIS software), with access to a secure package environment for the geospatial community. AUSTIN, Texas, April 12, 2022 (BUSINESS WIRE) – Today, Anaconda Inc. , provider of the world’s most popular data science platform, announced a collaboration with Esri, the global market leader in geographic information system (GIS) software, location intelligence, and mapping. This collaboration supports Esri and the geospatial community by providing users of Esri’s software with preloaded geospatial packages for use with Python. “Esri has been a long-standing customer, and we are excited to partner with them to provide a world-class Python integration for our joint users,” said Peter Wang, CEO and co-founder of Anaconda. “Geospatial analysts and data scientists can perform powerful new analyses using the rich ecosystem of tools within the Anaconda package repository while staying within their familiar ArcGIS environment. I am thrilled to better serve this broad user community that spans government agencies, health and services, transportation, education, and more. ” Following Anaconda’s launch of their Embedded Partner Program in November and their partnership announcement with Snowflake, this collaboration allows Anaconda to expand its footprint in location-based analytics and become the standard tool for enterprise Python. “By having a tailored environment, users will be able to complete data analysis with Python while also having a format that Data Scientists are familiar with and catering to a wide variety of users in the geospatial community,” said David Watkins, ArcGIS Pro... --- > Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. - Published: 2022-04-05 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/newsroom/anaconda-welcomes-python-thought-leaders-to-advance-technical-frontier - News Category: Company Anaconda is the birthplace of Python data science. We are a movement of data scientists, data-driven enterprises, and open source communities. Russell Keith-Magee and Antonio Cuni will support the company’s trajectory as a data science leader in Python AUSTIN, Texas--(BUSINESS WIRE)--Anaconda Inc. , provider of the world’s most popular data science platform, today announced the appointments of two long-time Python leaders, Russell Keith-Magee and Antonio Cuni. Russell Keith-Magee has been a part of the Python open-source community for almost 20 years. He joined the core team of the Django Project in 2005, later serving as President of the Django Software Foundation from 2010 to 2015. Keith-Magee founded the BeeWare Project in 2014, a project designed for developing tools and libraries for cross-platform native user interfaces in Python and expanding the availability of Python to mobile and browser platforms. In addition to his work across the Python community, Keith-Magee has worked at several startup companies like Wotnews, Hunted Media and TradesCloud throughout his career. “Anaconda has a long history of being an active participant in and contributor to the Python open source community, serving as a model for how a company can be commercially successful while maintaining the collaborative spirit of open source,” said Keith-Magee, Principal Software Engineer Team Lead at Anaconda. “I look forward to my future with Anaconda as I help the organization become more deeply involved with my true passion: advancing the Python community. ” For the past 15+ years, Antonio Cuni has been a core developer of PyPy, a fast, compliant Python interpreter. Cuni notably co-founded HPy, a project providing a new API for extending Python in C.... --- - Published: 2021-11-18 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/anaconda-launches-embedded-partner-program-as-demand-for-python-continues-to-soar - News Category: Company The program will enable organizations to seamlessly and securely integrate open-source Python into their own products, spurring innovation across industriesAUSTIN, Texas, November 18 (GLOBE NEWSWIRE) — Anaconda Inc. , with over 25 million users worldwide, officially launched an Embedded Partner Program today in response to the rising demand among businesses for access to secure, managed Python packages and environments. As part of the program, companies can embed Anaconda tools, packages, and repositories into their own products and services, with a seamless access experience for end users. Whether Anaconda is embedded behind the scenes to power a company’s solution or made available directly to a company’s customers, the Embedded Partner Program provides a reliable and secure way to manage Python environments and enjoy the innovations of open-source development with a trusted partner on their side. “At Anaconda, our number one focus is making open-source innovation within Python easily accessible to end users,” said Albert Gashi, Senior Vice President of Revenue at Anaconda. “As the demand for Python continues to grow, our embedded partners can equip their customers with curated packages and repositories, making it simple for them to offer a competitive solution that meets their business demands. ”Anaconda chose to formally launch the Embedded Partner Program now because enterprises want access to a supported open-source Python ecosystem for their customers, products, and services without having to worry about security, licensing requirements, or dependency management. This comes on the heels of Anaconda’s recent funding, including investments from Snowflake Ventures and open-source investment... --- - Published: 2020-06-17 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/press/datacamp-partners-with-anaconda-to-enable-data-fluency-for-teams - News Category: Company Anaconda Team Edition customers now have access to DataCamp training contentNEW YORK, June 17, 2020 /PRNewswire/ — DataCamp, the leading interactive learning platform for data science and analytics, today announced a new partnership with Anaconda, which provides open-source tools and enterprise-grade software for data scientists and organizations doing data science at scale. Inspired by a shared mission to enable worldwide data fluency for individual learners and organizations, Anaconda Team Edition customers will now gain access to a specialized DataCamp learning track that teaches the essentials of Conda, an open-source package management system for the Python coding language. “Anaconda is proud to partner with DataCamp on this initiative to bring interactive data science training to our customers,” said Angela Pierce, President and CFO at Anaconda. “With a wide variety of courses from beginner to intermediate, DataCamp is a great resource for Python and R training that will serve our user community well. ”“We’re excited to unite DataCamp’s online training platform for data science with Anaconda’s powerful suite of tools for corporations,” said Martijn Theuwissen, CEO at DataCamp. “The most successful companies know that you need both the infrastructure and systems, as well as a robust learning program, to effectively implement data science and analytics initiatives at scale. ”DataCamp supports data science and analytics training at scale through DataCamp for Business. It’s easy to implement and manage for teams and organizations of any size, with advanced analytics and insights, custom learning paths, and seamless SSO and LMS integrations. DataCamp is constantly expanding... --- --- ## Resources - Published: 2025-08-20 - Modified: 2025-08-21 - URL: https://stage.anaconda.com/resource/state-of-data-science-report-2024 Major Benefits 0 % of respondents ranked “most useful, most economical, and speed” as a top 3 value of open-source software. New Challenges 0 % 0 % feel unprepared or unsure about taking on rising challenges in the data science space, including government regulations, technological tools, and the increase in AI usage across roles. of respondents view security concerns around open source as the biggest technical challenge for AI adoption. AI-Powered Workflows 0 % of data science practitioners are spending as much or more time on AI techniques this year compared to last year. 0 % are using AI for data cleaning, visualization, and analysis. 0 % are using AI for automating tasks. 0 % are using AI for prediction or detection models. 0 % of respondents said they’re building new tools with AI. 0 % conduct most of their AI or data science training on either local laptops or local desktops or workstations. Top Areas of Eduction Among data science, machine learning, and IT student tracks: 0 % are learning Python. 0 % are learning machine learning techniques and implementation. 0 % are learning data visualization. 0 % are learning probability and statistics. Interested in learning more? Read the full 7th Annual State of Data Science and AI report. Get the Report --- - Published: 2025-08-08 - Modified: 2025-09-02 - URL: https://stage.anaconda.com/resources/case-study/entercard-accelerates-credit-risk-modeling-with-anaconda-and-snowflake - Resource Types: Case Studies Entercard Accelerates Credit Risk Modeling with Anaconda and Snowflake How a leading Nordic credit card company reduced model development time by 25% and slashed documentation from weeks to daysEntercard stands as one of the Nordic region's leading credit market companies, serving over 1. 7 million customers across Sweden, Norway, Denmark, and Finland. Founded in 2005 as a joint venture between Swedbank and Barclays Principal Investments, this Stockholm-based financial institution employs over 450 professionals representing more than 40 nationalities. When Nicholas Munford joined Entercard seven years ago, the company was relying on traditional statistical software for their critical credit risk modeling. As the foundation of their lending decisions, these models needed to be both highly accurate and rapidly deployable—but their existing toolchain was becoming a bottleneck. "We wanted to start using more sophisticated machine learning techniques like gradient boosted decision trees, which are very useful in our industry," explains Munford, senior decision science analyst who builds the statistical and machine learning models that assess credit worthiness for Entercard's applicants. "The support for those kinds of techniques in our previous platform was pretty patchy. "Finding the Right FoundationEntercard's decision science team faced a classic enterprise dilemma. While they needed access to cutting-edge open source packages for advanced analytics, their information security team wasn't comfortable with unrestricted access to open repositories. "We encountered some initial hesitation from our information security departments," Munford recalls. "They were happy to give us a Python installation, but understandably cautious about allowing us to install whatever open source... --- > Discover why 67% of organizations face AI deployment delays due to security concerns. Download our exclusive survey of 300+ AI leaders revealing critical governance gaps and solutions. - Published: 2025-08-06 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/resources/report/bridging-the-ai-model-governance-gap - Resource Types: Report Get the Complete Research Report Enterprise AI adoption is accelerating, but critical governance gaps threaten progress. Our survey of over 300 AI practitioners and decision-makers reveals where organizations excel and where dangerous blind spots remain. Discover the strategies leading enterprises use to balance innovation speed with risk management. Download the full report to learn: Why 67% of organizations experience AI deployment delays due to security issues How only 26% achieve truly unified AI toolchains—and why fragmentation kills governance Best practices from enterprises successfully scaling AI with comprehensive oversight Actionable frameworks to transform governance from roadblock to competitive advantage Download the complete Forrester TEI study and calculate your estimated ROI with Anaconda today. Download Report --- - Published: 2025-07-25 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/webinar/making-enterprise-ai-work/ - Resource Types: Webinar Enterprise AI projects are failing at alarming rates. Despite massive investments, most organizations struggle to move from proof-of-concept to production-scale success. The problem isn't the technology—it's the approach. Join Forrester Principal Analyst Mike Gualtieri, Forrester Senior Consultant, Luca Son, and Anaconda Field CTO Steve Croce as they reveal why some enterprises are thriving with AI while others remain stuck. You'll discover the real barriers blocking AI success and the practical strategies that are delivering measurable results. Key Takeaways: The 3 critical mistakes that doom AI projects before they start How to balance innovation speed with enterprise security requirements The unified platform approach that eliminates AI toolchain fragmentation Why curated open source beats DIY for production-ready AI at scale Whether you're a data leader frustrated by slow progress, an IT executive balancing innovation with security, or a business leader demanding AI results, this session provides the clarity you need to move forward with confidence. Meet Our Speakers Steve Croce is the Field Chief Technology Officer at Anaconda, where he bridges technical innovation with customer success for the world's leading Python platform for AI and data science. With over 20 years in the tech industry, he combines deep product strategy expertise with customer-facing technical leadership to drive transformative outcomes for Anaconda's 50 million users and thousands of enterprise customers. Mike's research focuses on AI technologies, platforms, and practices that enable technology professionals to deliver applications that lead to prescient digital experiences and breakthrough operational efficiency. His key technology coverage areas are AI... --- > Get the latest state of data science report from Anaconda. See the latest trends. - Published: 2025-07-17 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/resources/report/state-of-data-science-2019 Creating Context for Data Scientist and Developer Collaboration Explore Top Open-Source Tools and Use Cases Across Industries. Build, Deploy, and Maintain Secure AI SolutionsIn this report, we drill down into the developer and data scientist demographic from our 2019 State of Data Science survey of 5,000 users. From the data we find key differences and similarities between the two roles, including: Tool preferences Coding background Model delivery techniques In this report we address how these roles overlap in the data science life cycle and how we overcome tool diversity to ensure collaboration. Download Now --- > Learn what to watch out for when downloading open-source packages and how to establish a governance program to get more models into production. - Published: 2025-07-17 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/resources/guide/how-to-implement-an-oss-governance-program-for-data-science-security How to Implement an OSS Governance Program for Data Science, Artificial Intelligence, and Machine Learning What's Missing From Enterprise Data Science and Machine Learning? Security. Open-source software (OSS) is the backbone of data science and machine learning innovation. No single technology vendor can outmatch the open-source community. While OSS is generally safe, vulnerabilities creep in over time. Just like DevOps teams, data scientists need to develop processes that ensure they are evaluating, downloading, and monitoring software packages to minimize risk and meet IT security standards. Our How to Implement an OSS Governance Program for Data Science, Artificial Intelligence, and Machine Learning helps you do just that. Learn what you need to watch out for when downloading open-source packages and how to establish a governance program that allows you to get more models into production. Make sure your team is security-savvy. Submit Form to Get Started --- > Discover how Anaconda delivers 119% ROI over three years with significant security and time savings. Download the Forrester TEI study now to boost your organization's efficiency. - Published: 2025-07-02 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/report/forrester-tei-impact-of-anaconda - Resource Types: Report Forrester Study: The Total Economic ImpactTM of Anaconda Measurable ROI: Security, Savings & Efficiency Gains See how Anaconda delivered significant ROI for its customers, then use our interactive calculator to estimate what your company could achieve. Forrester Consulting, commissioned by Anaconda, conducted a Total Economic Impact analysis of our AI platform, and found that a composite organization representative of interviewed customers with experience using Anaconda realized an ROI of 119% over three years. The study highlights several key benefits of adopting Anaconda achieved by the composite organization, including: With Anaconda, organizations saved 83 hours per user annually on package security management Organizations reduced IT security administrator time by 1,660 hours Interviewee’s organizations strengthened security posture and reduced breach risks with the Anaconda AI Platform Organizations saved $80,000 annually in technology costs Download the complete Forrester TEI study and calculate your estimated ROI with Anaconda today. Read the Report & Calculate Your ROI --- > Discover how Conda simplifies package management for data science, ML, and AI. Learn why it's the easiest way to set up a functional Python environment. - Published: 2025-07-01 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/guides/conda-package-manager-for-data-sciences-ml-and-ai - Resource Types: Guides Python’s open-source ecosystem is a powerhouse of innovation, constantly introducing new tools and libraries that enable developers to enhance their software by leveraging community contributions. This rapid evolution is particularly evident in the machine learning (ML) and artificial intelligence (AI) sectors. However, this flexibility also brings challenges: managing package dependencies, handling multiple Python versions, and reproducing setups across various systems. While seasoned developers might navigate these hurdles with relative ease, scientific, ML, and AI users without formal backgrounds in software engineeringmay face significant friction. Conda helps Python users get over all of these hurdles and more. But despite being around for over a decade, conda remains surrounded by myths and misconceptions. This guide aims to clarify what conda truly offers and why it has become indispensable for users in the scientific, data science, machine learning, and AI communities. What is conda? Package managers typically handle installing, updating, and uninstalling software packages. Most are either language-specific (like pip for Python or npm for NodeJS) or system-specific (like homebrew for MacOS or apt for Debian Linux). While pip is synonymous with Python packages, conda is a different beast entirely. Conda is not just a Python package manager; it is an open-source, language-agnostic package and environment manager that works across all major operating systems and platforms. Conda provides a unified solution for managing environments and packages, streamlining workflows for developers and researchers working with complex, mixed-language stacks. While pip excels at managing Python packages, conda shines in handling dependencies involving compiled code from... --- > Learn how Python packages work and how to manage them, plus essential tools for open-source AI and data science. - Published: 2025-07-01 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/guides/python-packages - Resource Types: Guides Python has revolutionized modern software development, becoming the programming language of choice for millions of developers worldwide. From web applications to artificial intelligence, Python's versatility stems largely from its extensive ecosystem of packages—pre-built code modules that extend Python's core functionality and accelerate development workflows. Understanding how to effectively manage Python packages is crucial for optimizing development processes, ensuring project stability, and harnessing the full potential of this powerful programming language. Whether you're building machine learning models, developing web applications, or automating business processes, mastering Python package management will significantly enhance your productivity and project outcomes. What are Python Packages? A Python package is a structured collection of related modules organized within a directory hierarchy. Think of it as a folder system that contains multiple Python files (modules), each serving specific functions, all working together to provide comprehensive functionality for particular use cases. At its core, a package differs from a simple module in several key ways:ComponentDefinitionStructureExampleModuleA single Python file containing codeSingle . py filemath_utils. pyPackageA directory containing multiple modulesFolder with __init__. py and multiple . py filesdata_analysis/ directoryLibraryA collection of packages and modulesMultiple packages working togetherNumPy, Pandas, SciPyThe __init__. py file plays a crucial role in Python packages. This special file tells the Python interpreter that a directory should be treated as a package, enabling proper importing and initialization. When you import a package, Python executes the code in __init__. py, which can define what gets imported when someone uses from package import *. Understanding Python Package StructurePython packages follow... --- > Unlock AI success implementation with Forrester’s Brandon Purcell and women tech leaders. Learn game-changing AI strategies. Watch on-demand now! - Published: 2025-07-01 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/resources/webinar/women-illuminating-path-ai-success - Resource Types: Webinar Join Forrester analyst Brandon Purcell and women tech leaders as they share proven strategies for building organization-wide AI capabilities. This discussion offers practical insights for accelerating AI adoption while ensuring responsible implementation. Key learnings include: Building trust and drive AI literacy across teams through collaborative learning networks Bridging technical-business gaps with structured knowledge sharing and governance Creating scalable frameworks for AI education and skill development Transforming organizational culture to support sustainable AI innovation Meet Our Speakers Watch Now --- > PNC ditched proprietary tools for Anaconda Enterprise, empowering teams bank-wide with open-source Python and cutting processing time by 90%. - Published: 2025-06-30 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/resources/case-study/pnc-financial-services - Resource Types: Case Studies How PNC Financial Services Enables Data Science and Machine Learning Capabilities using Anaconda PNC, a banking and financial services corporation operating in 19 states. Data Manager Ann Manchella and Data Scientist Jim Ogle regaled the AnacondaCON crowd with the story of how they went about building a data science “competency center” to enable data science and machine learning capabilities across the company. Making Python a First-Class CitizenBack in 2015, PNC created a new Enterprise Data Management team that relied primarily on a proprietary data science platform. Based on their experiences, the team took it upon themselves to convince management to make the switch to open source analytics and make Python a first-class citizen in their analytics environment. The main argument at the time was that using open source Python and R in lieu of SAS and other commercial alternatives would, of course, dramatically reduce software costs. But there were other compelling arguments to support their case. The team found that Python allows for easier debugging, decreased development time, and improved performance. And the explosive growth of Python in recent years meant a larger pool from which to recruit new talent, a strong user base to provide online community support, and easily accessible, inexpensive training. Furthermore, Python and R include a huge collection of libraries—to use for everything from machine learning to visualization—that support the full analytics lifecycle. Choosing Anaconda Enterprise as PNC’s AI Enablement PlatformNext, the team needed to choose an AI platform that would support their open source aspirations,... --- - Published: 2025-06-25 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/webinar/2025-ai-predictions - Resource Types: Webinar What’s Next in AI? Register to access our exclusive on-demand webinar, 2025 AI Predictions Webinar, where top experts from Anaconda and the industry come together to explore the trends, innovations, and opportunities shaping the future of AI. Our panel will cover the latest insights on AI agents, infrastructure, ethical challenges, and strategies to leverage AI for business growth in the year ahead. Following the discussion, we’ll open the floor for a 30-minute live Q&A—, giving you the opportunity to ask questions and engage directly with our panelists. What You’ll Learn: AI Agents: How autonomous agents will drive automation and business operations. AI Trends for 2025: What cutting-edge advancements to prepare for. Real-World Applications: How organizations are turning AI insights into action. Practical Advice: How businesses can align their AI strategy for the coming year. Meet the Panel: Our panel of experts features two Anaconda SMEs and two external thought leaders from the industry, moderated by an Anaconda leader. Together, they’ll explore predictions and answer your most pressing questions. Moderator: Kodie Dower, Sr. Marketing Communications Manager, Anaconda Panelists: Peter Wang, Chief AI Officer, Anaconda Greg Jennings, Head of Engineering - AI, Anaconda Priyanka Kulkarni, founder and CEO of Casium David Pitman, Engineer Leader in AI & Startup/VC Advisor Why Attend? Stay Ahead of the Curve: Get exclusive insights into 2025 AI trends. Ask the Experts: Engage in a live Q&A with panelists. Learn Actionable Strategies: Align your business with AI’s evolving landscape. Watch Webinar OnDemand --- > Discover the top machine learning open-source software to boost your AI and data science projects. - Published: 2025-06-17 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/guides/machine-learning-software - Resource Types: Guides Machine learning (ML) is no longer just a concept for forward-looking tech teams—it’s quickly becoming a cornerstone of innovation for businesses across industries. Yet, for many teams, knowing where to begin with ML software can feel overwhelming. The good news? Open-source tools offer an accessible starting point, empowering users to build models, analyze data, and unlock insights without the high costs of proprietary solutions. Python, one of the most popular languages for ML, stands out for its simplicity and adaptability across diverse use cases. Its robust open-source ecosystem has developed useful libraries like TensorFlow and scikit-learn, making it easier than ever to design and deploy ML models. Whether your team is exploring ML for the first time or looking to refine your approach, the right open-source tools—paired with Python—can deliver real value and set the stage for long-term success. Let’s take a closer look at some of the best tools available and how they can elevate your ML capabilities. What is Machine Learning? Machine learning is a subfield of artificial intelligence (AI) that allows computers and machines to learn from data without explicit programming. ML systems automatically improve their performance through experience by identifying patterns in the provided data. Supervised learning  Supervised learning is one of the most common types of ML. With supervised learning, the algorithm learns from labeled data to make better predictions or decisions with new data. The goal is to find a function that maps inputs to outputs with as little error as possible. Some examples of supervised learning include... --- - Published: 2025-06-15 - Modified: 2025-08-29 - URL: https://stage.anaconda.com/topics/open-source-ai - Resource Types: Topics The artificial intelligence landscape is rapidly evolving, with open source AI emerging as a transformative force that’s democratizing access to cutting-edge AI technologies. As organizations increasingly adopt AI-powered applications, understanding the ecosystem of open source AI models, tools, and frameworks has become essential for developers, data scientists, and business leaders alike. Unlike proprietary solutions that operate as closed systems, open source AI provides unprecedented transparency, flexibility, and collaboration opportunities. This comprehensive guide explores what defines open source AI, examines its benefits and challenges, and provides practical guidance for leveraging these powerful tools in your AI projects. What Is Open-Source AI? Open source AI represents a fundamental shift in how artificial intelligence technologies are developed, distributed, and implemented. According to the Open Source Initiative, open source AI encompasses AI systems where the source code, training data, model weights, and documentation are freely available for anyone to use, modify, and distribute. This approach stands in stark contrast to closed source AI systems like ChatGPT’s underlying models, where the inner workings remain proprietary. Open source AI promotes transparency, encourages global collaboration, and accelerates innovation by allowing researchers and developers worldwide to contribute to and build upon existing work. The open source AI ecosystem includes everything from large language models (LLMs) like Meta’s Llama and Microsoft’s Phi models to specialized tools for computer vision, natural language processing, and machine learning workflows. Major platforms like Hugging Face have become central hubs for sharing and accessing these open source ai models, creating a vibrant community of practitioners... --- > Discover the top agentic AI tools, key features to look for, and best practices for developing and deploying AI agents for your organization. - Published: 2025-06-05 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/guides/agentic-ai-tools - Resource Types: Guides The AI landscape is rapidly evolving, with agentic AI tools leading the charge in creating autonomous systems that can reason, plan, and execute complex tasks with minimal human oversight. Unlike traditional AI that focuses solely on content generation, agentic AI enables software to make decisions, interact with multiple systems, and streamline workflows through intelligent automation. This comprehensive guide explores the top agentic AI tools and frameworks available in 2025, their key features, and practical applications across industries—helping you select the right tools to power your AI initiatives. What Are Agentic AI Tools and Why Are They Valuable? Agentic AI tools are software frameworks and platforms that enable developers to build autonomous AI systems capable of performing complex tasks independently. These tools go beyond simple content generation to create AI agents that can: Process information from multiple sources Make data-driven decisions Execute multi-step workflows across different applications Learn and adapt from their interactions Solve problems with minimal human intervention Unlike generative AI tools that primarily focus on creating content based on prompts, agentic AI tools create systems that actively perceive, reason, act, and learn—functioning more like autonomous digital assistants than passive content generators. To fully appreciate agentic AI tools, it’s essential to understand how they differ from the generative AI applications that have become mainstream. FeatureAgentic AIGenerative AIPrimary FocusExecuting actions and making decisionsCreating content (text, images, code)Autonomy LevelHigh – operates with minimal supervisionLow – requires specific prompts and guidanceIntegrationConnects with multiple systems and APIsTypically functions as a standalone toolWorkflow ComplexityHandles multi-step,... --- - Published: 2025-06-03 - Modified: 2025-08-28 - URL: https://stage.anaconda.com/guides/machine-learning-libraries - Resource Types: Guides Machine learning (ML) is revolutionizing industries, driving advancements in healthcare, finance, retail, and beyond. From personalized recommendations to fraud detection, ML has become an essential tool for solving complex problems and enhancing decision-making. However, selecting the right libraries for your team can be daunting. With countless options available, finding the right fit for your tech toolset — or replacing underperforming tools — can feel overwhelming and might even stall the growth of your enterprise’s ML program. Python serves as a go-to language for machine learning and data science, thanks to its extensive library ecosystem. Whether you’re building predictive models or exploring deep learning, you must identify the right Python libraries to empower your team and future-proof your ML infrastructure.   Anaconda simplifies the process of adopting and managing these libraries. With its pre-configured environments and seamless integration with Python, Anaconda ensures your team can focus on innovation rather than setup, making it an invaluable tool for streamlining ML adoption. To help the decision-making process, this guide presents a curated list of top ML libraries, their applications, and how tools like Anaconda can streamline adoption. scikit-learn Best suited for: Classical machine learning tasks such as data preprocessing, model selection, and evaluation. Example use case: Companies often use scikit-learn for customer segmentation or recommendation systems, helping them better understand and meet customers’ needs. scikit-learn is one of the most widely used libraries for implementing ML algorithms. Its simple and efficient design makes it an ideal choice for tasks ranging from exploratory data analysis to building predictive... --- - Published: 2025-05-19 - Modified: 2025-08-29 - URL: https://stage.anaconda.com/topics/what-are-ai-agents - Resource Types: Topics As artificial intelligence transforms industries worldwide, understanding AI agents—their types, functions, and real-world applications—has become essential for leveraging their full potential in business operations, customer experiences, and technological innovation. What Are AI Agents? AI agents are autonomous computer systems designed to perceive their environment, process information, make decisions, and take actions to achieve specific goals. Unlike traditional software programs that execute predefined commands, AI agents can operate independently, adapt to changing conditions, and improve performance over time without direct human supervision. These intelligent systems range from simple rule-based applications to sophisticated platforms capable of complex problem-solving, learning from experience, and even collaborating with other AI systems or humans. Whether optimizing supply chains, personalizing customer interactions, or assisting with medical diagnoses, AI agents are fundamentally changing how we approach complex tasks across industries. Modern AI agents often leverage generative AI capabilities to produce content, solutions, and responses that weren’t explicitly programmed. By utilizing advanced AI models trained on vast datasets, these agents can create original outputs that address specific needs, significantly expanding their utility and effectiveness in real-world applications. How Do AI Agents Work? AI agents function through a structured cycle of perception, processing, and action: 1. Perception The agent collects data from its environment using various inputs, which may include: Digital interfaces (APIs, web services, databases) Sensors (cameras, microphones, IoT devices) User interactions (text, voice, or behavioral data) System logs and performance metrics 2. Processing Once data is collected, the agent analyzes it using: Machine learning models (including neural networks) Knowledge... --- > AI is poised to make a huge economic and reshape the competitive landscape in every major industry. To realize this value, you must operationalize machine learning. - Published: 2025-04-28 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/why-you-need-ml - Resource Types: Whitepaper Here’s Why You Need a Machine Learning Platform to Stay on Top The predicted economic impact of AI is huge. It’s also a game-changer that can reshape the competitive landscape in every major industry. McKinsey predicts deep learning will generate up to $5. 8 trillion in annual economic value. But, for your business to realize any of this value, you have to operationalize machine learning. Download our whitepaper, “Why Your Business Needs a Machine Learning Platform” to learn about the growth of AI across industries and why you need a platform to operationalize the machine learning lifecycle. Get Your Free Copy --- > Choose the right platform to accelerate your ML life cycle with an overview of key considerations and checklist of infrastructure, security, and integration features. - Published: 2025-04-28 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/whitepaper/the-complete-data-science-platform-buyers-guide - Resource Types: Whitepaper The data science and machine learning platform space is dynamic and crowded. In addition to understanding what the market has to offer, shopping for a platform means assessing the needs of your data science, IT, and leadership teams. There’s a lot to contemplate before choosing the right platform to accelerate your machine learning life cycle. Our buyer’s guide helps you through the process with an overview of key considerations and an interactive checklist of 60+ infrastructure, security, and integration features. Fill out the form to get started. Get Your Free Copy --- > Open Source Tools & Libraries - Published: 2025-04-28 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/os-tools - Resource Types: Whitepaper The world of open-source tools and libraries is vast and difficult to navigate. With hundreds of thousands of packages available for unique purposes, how do you know where to look for what you need? Our guide to open-source tools will help any newcomer get started, and if you’re a veteran data scientist, you may discover some helpful tools you didn’t know about. Download Anaconda’s Guide to Open Source to start exploring. Get Your Free Copy --- > Learn about what it takes to become a data scientist. What you should learn in school and what kind of additional skills you will need to succeed. Learn more. - Published: 2025-04-24 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/whitepaper/how-do-i-become-a-data-scientist - Resource Types: Whitepaper According to LinkedIn, there has been a 37% annual growth in hiring for data scientist jobs between 2015-2019. The profession has topped their Emerging Jobs list for three years running, and the platform sees this level of rapid growth in data science roles across all industries. Businesses and organizations of all kinds now understand the value of good data and those that can wrangle, make decisions from, and communicate it. In this report you will get answers to: What should you study in school? What if you don’t have a background in STEM? Do you need continued education? What technical skills are important in data science? What soft skills will help you excel? How do you get real world experience? Download --- > Deep Learning - Published: 2025-04-14 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/deep-learning - Resource Types: Whitepaper Deep learning (DL) is a form of artificial intelligence that utilizes neural networks and outperforms traditional machine learning in compute-intensive tasks such as image recognition and natural language processing. Until quite recently, only behemoths like Amazon or Google could afford to implement deep learning at scale. Today, most any company with a data scientist can get started with deep learning projects. Our white paper discusses the democratization of GPU technology and provides an introduction to how to select the right combination of compute infrastructure, on-prem or in the cloud, for your data science team. Get Your Free Copy --- > How to Implement an OSS Governance Program for Data Science, Artificial Intelligence, and Machine Learning - Published: 2025-04-09 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/data-science-security - Resource Types: Whitepaper What’s Missing From Enterprise Data Science and Machine Learning? Security. Open-source software (OSS) is the backbone of data science and machine learning innovation. No single technology vendor can outmatch the open-source community. While OSS is generally safe, vulnerabilities creep in over time. Just like DevOps teams, data scientists need to develop processes that ensure they are evaluating, downloading, and monitoring software packages to minimize risk and meet IT security standards. Learn what you need to watch out for when downloading open-source packages, and how to establish a governance program that allows you to get more models into production. Make sure your team is security-savvy. Fill out the form to get the whitepaper. Get Report --- - Published: 2025-04-09 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/whitepaper/dan-meador-free-book-chapter - Resource Types: Whitepaper About this report Building Data Science Solutions with Anaconda is a comprehensive starter guide to building robust and complete models. The book covers everything you need to know about algorithm families and helps you build must-have skills such as building interpretable models and avoiding bias in data. By the end of the book, you’ll be able to confidently use conda and Anaconda Navigator to manage dependencies, and you’ll have gained a thorough understanding of the end-to-end data science workflow. This free chapter, “Dealing with Common Data Problems,” covers the following topics:Dealing with too much dataFinding and correcting incorrect data entriesWorking with categorical values with one-hot encodingFeature scalingWorking with date formatsYou may also purchase the book. About the Author Dan Meador”Professional nerd” is Dan Meador’s response whenever anyone asks what he does for a living. It wasn’t always so clear-cut when he played football for the Arkansas Razorbacks as he was getting his degree in computer engineering, but nowadays the pendulum has swung pretty clearly into the “nerd” classification. Over a decade working in Fortune 5 companies and later finding startups to be more liking, he’s seen firsthand how the power of data can help ask better questions and guide better solutions. He holds a patent for his work on AI systems and has been able to grow his experience in AI/ML by building AutoML solutions. His journey has also taken him to the Pentagon where he was able to present his work on AI systems. Dan currently is an engineering... --- > Leveraging the power of open-source software across an enterprise organization requires capabilities for building and deploying secure Python solutions. This guide explores what to consider when choosing an enterprise platform to collaborate and build powerful applications with data science and machine learning. - Published: 2025-04-09 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/selecting-an-enterprise-platform-for-python-and-open-source-a-checklist-for-buyers - Resource Types: Guides IntroductionMarc Andreessen famously opened an August 2011 blog article with this provocative sentence: “Software is eating the world. ” His prediction was that software development would disrupt traditional industries. Indeed, companies like Airbnb, Netflix, and Uber emerged as just a few of many winners in the “on-demand” economy that disrupted industries like travel, entertainment, and shopping in significant and lasting ways. About a year later, in October 2012, Harvard Business Review reported that data scientist was the “sexiest job of the 21st century,” promising professionals who could “coax treasure out of unstructured data. ” And the race to structured data began, with organizations taking a closer look at their messy data and finding ways to make it more consumable by machines. Fast-forward seven years to October 2019, and McKinsey Global Institute offered exciting and cautionary words about the “coming of AI spring. ” Their research showed hundreds of business cases that, combined, had the potential to create between $3. 5 trillion and $5. 8 trillion in value annually. As organizations applied artificial intelligence, they found it could yield outsized business value. Data science capabilities emerged as a prerequisite for high-performing AI, so organizations increased their investments in technologies, data science teams, and techniques like machine learning and deep learning. In August 2022, Stable Diffusion rocked the visual arts world with its text-to-image model built with Python and deep learning that could generate detailed images based on text prompts. It stoked the world’s fascination with AI and unleashed its next wave:... --- > AI offers unprecedented opportunities for enterprise organizations. We created this guide to be a handy reference about opportunities, challenges, and best practices of AI for enterprise. - Published: 2025-04-09 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/guides/leveraging-ai-for-enterprise-success - Resource Types: Guides Artificial intelligence (AI) stands at the forefront of technological evolution, offering unprecedented opportunities for enterprise organizations. AI is not just a technology or a tool—it is a pivotal cornerstone that will define the future of enterprises. Its applications transcend traditional boundaries, offering transformative solutions across various business functions. For example, over the past two years, generative AI models and large language models (LLMs) have taken the market by storm. This leap in technology has enabled more natural and intuitive user interactions, powering everything from advanced customer service chatbots to sophisticated content creation tools. Over the last year, OpenAI’s ChatGPT became one of the fastest-growing internet products, according to Air Street Capital’s State of AI Report 2023. The emergence of ChatGPT and similar LLMs signifies a significant shift towards more intelligent, efficient, and personalized AI tools, reshaping how we interact with technology on a fundamental level. We’ve created this guide to be a handy reference about the AI opportunity, challenges, and best practices. Please bookmark this page and share it with colleagues if you find it helpful. IntroductionIn this guide, we will cover the opportunities, challenges, and best practices for organizations seeking to leverage AI in the enterprise. Gathering AI insights and a deeper understanding of this technology is pivotal for leaders, especially C-level executives, who are looking to harness its potential. In this article, we outline the opportunities, challenges, and best practices in applying AI, backed by research and data on its impact in the enterprise sector. Finally, we will... --- > Get the key considerations for evaluating a data science platform (in-house or external) and determine the right platform for your company, in this whitepaper from Anaconda. - Published: 2025-04-07 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/build-vs-buy - Resource Types: Whitepaper Virtually every industry has embraced data science and machine learning as the next frontier for growth and innovation. Whether you have a full-fledged data science team building machine learning models or not, you’re probably wondering how to operationalize and scale your data science program. Our build versus buy whitepaper highlights the key considerations for evaluating a data science platform (in-house or external) and helps you determine the right platform solution for your company. We also include a data science platform Total Cost of Ownership (TCO) calculator to help you understand the cost of building your own platform. Fill out the form to get started. Get Your Free Copy --- - Published: 2025-03-28 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/enterprise-ml - Resource Types: Whitepaper Creating Context for Data Scientist and Developer Collaboration n this report, we drill down into the developer and data scientist demographic from our 2019 State of Data Science survey of 5,000 users. From the data we find key differences and similarities between the two roles, including: Tool preferences Coding background Model delivery techniques In this report we address how these roles overlap in the data science life cycle and how we overcome tool diversity to ensure collaboration. Get Your Free Copy --- > Explore top open-source tools and use cases across industries. Act strategically to empower practitioners and teams to build, deploy, and maintain secure AI solutions. - Published: 2025-03-26 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/definitive-guide-to-ai-platforms - Resource Types: Guides Artificial intelligence (AI) is undeniably and rapidly transforming life as we know it. Leading tech companies are investing heavily, and Google CEO Sundar Pichai has compared the impact of AI to electricity and fire. Organizations that have adopted AI to improve products and services are seeing significant financial returns on their investments. We are just beginning to see the promise of AI come to life. However, realizing business value with AI remains an elusive target for many organizations. Tools are ever-changing, use cases are disparate and scattered across the enterprise, and it’s incredibly complex to deploy useful AI into production. Data science and engineering teams that want to apply machine learning and deep learning to solve business problems face a confusing array of platforms, libraries, and software. Evaluating and making decisions about how to apply the latest tools and techniques to develop, deploy, and maintain high-performing models requires a cross-functional effort among people, strategic investments in technology, and constant recalibration of processes. We’ve created this guide to be a handy reference about AI platforms, common use cases, and factors to consider when evaluating AI platforms. Please bookmark this page and share it with colleagues if you find it helpful. IntroductionIn this guide, we will cover AI platforms for open-source data science and machine learning. We will define and consider the pros and cons of AI platforms that can facilitate your development and deployment of data science and machine learning algorithms, models, and systems. We will explore common use cases among... --- > Join us for an exclusive Tech Talk with Anaconda’s Chief AI Officer, Peter Wang! In this interactive Q&A session. - Published: 2025-03-18 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/webinar/tech-talk-caio-unplugged - Resource Types: Webinar Join us for an exclusive Tech Talk with Anaconda’s Chief AI and Innovation Officer and Co-founder, Peter Wang. In this interactive Q&A session, Peter shares expert insights on Python, AI, and the latest industry trends while answering key questions from participants. The discussion is moderated by Javvi Joyce Ferrer, Lifecycle Marketing Manager at Anaconda. Don’t miss this opportunity to learn directly from a leading AI expert! Meet Our Speakers Peter Wang is the Chief AI and Innovation Officer and Co-founder of Anaconda. Peter leads Anaconda’s AI Incubator, which focuses on advancing core Python technologies and developing new frontiers in open-source AI and machine learning, especially in the areas of edge computing, data privacy, and decentralized computing. Javvi Joyce Ferrer is a data-driven lifecycle marketing leader specializing in customer retention, engagement, and revenue growth. As Manager of Lifecycle Marketing at Anaconda, Javvi uses analytics to optimize communication journeys, improve conversions, and foster stronger user relationships for better retention. Watch Now --- > Discover the key differences between data science and data analytics, understand their unique applications, and learn how AI is transforming both fields. - Published: 2025-02-27 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/data-science-vs-data-analytics - Resource Types: Topics IntroductionOrganizations are constantly looking for ways to leverage their data to save time, reduce costs, and accelerate innovation. However, there are many different approaches to consider for generating valuable insights with data. By understanding which approach better suits specific use cases, you can maximize the impact of future data and AI initiatives. Data science and data analytics are separate yet related fields with distinct goals, tools, technologies, and required skills. Data science uses statistical and computational methods to extract insights from data, build predictive models, and develop new algorithms, while data analytics involves analyzing data to gain insights and inform business decisions. In this article, we’ll cover the key difference between data science and data analytics so you can understand which field better suits your use case. What Is Data Science? Data science is a multidisciplinary field that utilizes various tools and techniques to extract knowledge and insights from data. By combining statistics, programming, and domain expertise, data scientists can analyze and interpret complex datasets to uncover actionable insights. Some key activities of data science include:Data acquisition and wrangling: Collecting, cleaning, and preparing data for analysis. Exploratory data analysis: Identifying patterns and trends within large datasets. Model building: Using algorithms to create models that make predictions on new data. Data visualization: Communicating insights effectively using charts, graphs, and more. Data scientists use programming languages like Python and R to perform analysis and build AI/ML models. Python is a particularly popular language for data science, data analytics, and AI because it... --- > Understand the difference between data science and AI, and explore real-world examples of how they complement each other. - Published: 2025-02-27 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/data-science-and-ai - Resource Types: Topics IntroductionTwo terms that often arise in discussions around leveraging data are data science and machine learning. While these concepts are closely related (and sometimes mistaken to be the same), it’s important to understand the differences between data science and machine learning and their distinct characteristics and applications. Understanding the ways in which data science and machine Machine learning is a subAs organizations continue to collect and generate enormous amounts of data, they are faced with an urgent need to understand patterns within the data that lead to business insights. Data science and AI have emerged as critical methods for leveraging this data to make informed decisions, drive innovation, and maintain a competitive edge. In this article, we’ll explain the distinction between data science and AI, and how these fields complement each other. We’ll also cover some ways data science and AI are currently being used in the real world. What is Data Science? Data science is a multidisciplinary field that focuses on extracting insights from data using various tools and techniques. By leveraging statistics, programming, and domain expertise, data scientists can analyze and interpret complex datasets to uncover actionable insights for businesses. Key activities of data science include:Data collection: Gathering data from databases, cloud data stores, and other data sources that are relevant to a specific use case. Data cleaning: Preparing data for analysis by fixing or removing data that is incomplete, poorly formatted, or duplicated. Data analysis: Interpreting data using statistics, machine learning models, and other methods. Data visualization:... --- - Published: 2025-02-21 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/webinar/automate-analysis-snowflake-anaconda - Resource Types: Webinar About This Webinar Learn the power of Snowflake SQL and Anaconda Python packages to simplify tasks that often require advanced tools or technical expertise. Get straightforward examples that help you implement these workflows with confidence. Understand how Snowflake can streamline analytics, unlocking new capabilities. Key topics include How to segment customers effectively (using requests) How to build a growth accounting framework (using pandas) How to perform accurate sales forecasting (using prophet) Meet Our Speakers Jason Freeberg is a Product Manager at Snowflake, specializing in developer tools and cloud services. He leads key initiatives for the Snowpark developer platform, focusing on enhancing its capabilities and user experience. Will Luna is an Analytics Engineer at Anaconda, where he focuses on telemetry for data science and AI products. He has seen the common challenges that data teams face in driving business value across several SaaS startups. Watch Now --- - Published: 2025-02-13 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/resources/report/state-of-data-science-report-2024 - Resource Types: Report State of Data Science 2024 Report AI and Open Source at Work This 7th annual report reveals insights about the data science community’s demographics, industry use cases, and trends related to artificial intelligence (AI), and open source at work. Learn more about these themes in the report: AI-Powered Transformation 87% of practitioners are increasing AI adoption, with breakthrough applications in data cleaning, task automation, and predictive modeling. AI Workforce Revolution 49% of companies are adding AI Data Analysts, and 46% creating new AI Engineering roles to proactively hire for AI growth. AI Security Gap 42% of organizations cite security as their main AI challenge About the Survey Respondents 3,000 + Practitioners 136 Countries Get the Report --- > Learn more about how enterprise AI can be applied to various use cases within your organization. - Published: 2025-02-13 - Modified: 2025-08-07 - URL: https://stage.anaconda.com/topics/enterprise-ai-use-cases/ - Resource Types: Topics In recent years, AI has become an essential component of many companies’ technology stacks. While there’s no shortage of hype surrounding AI tools, understanding their real-world applications is key for organizations looking to maximize their ROI. This article explores practical examples of enterprise AI use cases across industries, offering insights to help you identify how AI can drive value and innovation within your business. AI in Financial ServicesFinancial AnalysisFinancial institutions regularly use AI to improve their analytical capabilities. For example, at JPMorgan Chase, AI systems analyze financial data and market trends to help investors make more accurate investment decisions. These AI tools can also analyze multiple reports, articles, and social media posts to identify patterns that human analysts might overlook. Budget ForecastingAI tools can help find relevant patterns in data and then use those patterns to make forecasts. What we refer to as “big data” is actually an assembly of smaller datasets, each revealing patterns when analyzed. These patterns are informative, and even when the narratives in individual datasets don’t align, assembling multiple analyses can provide useful insights. In another example, Microsoft noted impressive growth in its financial forecasts due to its AI-based tools that evaluate hundreds of variables, including market conditions and historical expense patterns. AI in GovernmentFraud Detection and PreventionWorldwide governments use AI to manage financial fraud and other security threats. For example, the U. S. Internal Revenue Service uses AI systems to flag suspicious tax returns by comparing them against known patterns of tax evasion. Social services... --- > Get Anaconda’s predictions for what the next 10 years of data science will bring. Keyword: future of data science - Published: 2025-02-13 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/future-of-data-science - Resource Types: Topics Data science is evolving at a breakneck pace, with AI quickly transforming the way businesses operate and deliver value to customers. This rapid change brings fresh challenges and exciting opportunities for both newcomers and seasoned data pros. To thrive and stay competitive in this dynamic landscape, keeping up with these advancements is essential. That’s why it’s critical for data science leaders and practitioners to keep an eye on emerging trends. Following data science trends and predictions can drive conversations around specific data science skills to learn, technologies to adopt, hiring decisions, and more. Read on to discover how Anaconda believes the next decade of data science will unfold. 8 Data Science Predictions: What Will the Next 10 Years Bring? Here are eight predictions based on emerging trends we believe will shape the next decade of data science:Deep learning for predictive analytics: Although deep learning has been popularized by generative AI, applying these rapidly advancing techniques to predictive analytics is also growing. Analyzing large volumes of unstructured data like images, audio clips, and text rather than just structured historical datasets can improve predictions and unlock new insights for businesses. AI & data regulations: There is already an increasing focus on data ethics and privacy, particularly as generative AI leverages more and more data for training and inferencing. Explainability and transparency will also be vital as AI models become more complex, especially in regulated industries like healthcare and financial services. AI and data regulations will continue to evolve as concern grows from... --- - Published: 2025-01-30 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/webinar/apple-coremltools-lp - Resource Types: Webinar About this Webinar Machine learning has gone beyond the data center and is now being deployed to the devices we carry with us every day. It is becoming increasingly important for data scientists to understand how to prepare models for use on mobile devices and wearables. In this on-demand webinar, we will show you how to use the open source coremltools project to convert your Python models to Apple’s Core ML format, enabling them to be used on everything from Apple watches to desktop computers. We will work through several examples and discuss tips and tricks to make working with Core ML from Python easier. Meet Our Speaker Stan led the Community Innovation team at Anaconda, where his work focused on high-performance GPU computing and designing data analysis, simulation and processing pipelines. He is a longtime advocate of the use of Python and GPU computing for research. Prior to Anaconda, Stan served as Chief Data Scientist at Mobi, where he worked on vehicle fleet tracking and route planning. Stan received a PhD in experimental high energy physics from the University of Texas at Austin, and performed research at Los Alamos National Laboratory, University of Pennsylvania and the Sudbury Neutrino Observatory. Watch Now --- - Published: 2025-01-30 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/webinar/graph-analytics-for-data-scientists - Resource Types: Webinar Analyzing data using conventional statistical methods involves looking at tabular data where data points are independent of each other, e. g. a person’s age is independent of any other person’s age. These approaches limit the insight that can be gained as there’s often knowledge hidden in how one data point relates to another. For example, two people can be deemed similar if they have entirely different purchase histories but each have purchase histories similar to a third user. This talk will go over how graph analytics can gain these sorts of insights not easily achievable through conventional data analysis performed on tabular data. Meet Our Speakers Paul was an open-source software engineer at Anaconda, where he worked on Metagraph, a hardware backend independent graph algorithm solver. Prior to Anaconda, Paul was at Cycorp, where he worked on methods to enhance data insights via knowledge graph integration. Watch Now --- - Published: 2025-01-30 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/webinar/building-an-oss-governance-program-for-ml-in-the-enterprise - Resource Types: Webinar About this Webinar Open-source software (OSS) is at the heart of many of the innovations in machine learning (ML) due to its accessibility, flexibility, and active community support. As a result, many enterprise organizations are adopting or seeking to adopt OSS into their operations, product development, and marketable solutions. However, as with any software, OSS is not immune to vulnerabilities and requires an actively managed governance program. In this webinar, we describe some of the major advantages of open-source software, along with common risks. We also describe key features of an effective OSS governance program with a focus on challenges for enterprise in the areas of software supply-chain security, license management, and support. Meet Our Speaker Cheng Lee is a Principal Software Engineer on the Anaconda Distribution Team. He holds a B. Sc and M. Sc in electrical engineering from UT Dallas and has spent the last two decades developing algorithms and software systems for managing and analyzing high-throughput biomedical data. Watch Now --- - Published: 2025-01-30 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resource/secure-by-design-how-conda-signature-verification-secures-your-software-pipeline-from-the-start - Resource Types: Webinar About this Webinar In this session, we discuss the content trust features in conda and the Anaconda Professional Repository. We’ll describe the design of conda’s package signature verification functionality and its trust architecture, and identify the threats they protect against. Finally, we’ll highlight what’s next for Anaconda content trust as we continue to work to better secure the conda package ecosystem. Meet Our Speaker In their role as Senior Security Engineer at Anaconda, Sebastien are responsible for packaging supply chain security. Prior to Anaconda, Sebastien was part of NYU’s Secure Systems Lab. There, they maintained and developed for The Update Framework (TUF), a security framework for package managers, and in-toto, a supply-chain security framework. Sebastien also helped develop Uptane, an extension and adaptation of TUF for the automotive sector, wrote Uptane’s reference implementation, worked on the Uptane Specification, and worked with folks in the automotive industry to achieve broad adoption. --- - Published: 2025-01-30 - Modified: 2025-07-02 - URL: https://10.2.107.56:8443/resources/webinar/data-driven-manufacturing-ai-ml-and-data-science - Resource Types: Webinar About this Webinar AI techniques like machine learning (ML) and deep learning (DL) offer new ways to optimize processes, automate workflows, detect anomalies, and reduce costs in manufacturing industries, with a potential value estimated at $2 trillion. However, these techniques can be tricky to implement and apply, with many potential pitfalls for the unwary. In this webinar, we’ll explore some use cases for AI in manufacturing and highlight what data science and machine learning practitioners need to consider when applying AI to this field. Meet Our Speaker Jim Bednar is the Director of Custom Services at Anaconda, Inc. Dr. Bednar holds a Ph. D. in Computer Science from the University of Texas, along with degrees in Electrical Engineering and Philosophy. He has published more than 50 papers and books about the visual system and about software development. Dr. Bednar manages the open-source Python projects HoloViz, Panel, hvPlot, Datashader, HoloViews, GeoViews, Param, Lumen, and Colorcet. Before Anaconda, Dr. Bednar was a lecturer and researcher in Computational Neuroscience at the University of Edinburgh, Scotland, and a hardware engineer working on data acquisition at National Instruments. --- - Published: 2025-01-30 - Modified: 2025-08-12 - URL: https://stage.anaconda.com/resources/whitepaper/efficient-data-prep-with-python - Resource Types: Whitepaper About this report Data discovery and data preparation have always been among the most time-intensive steps of a research project and for good reason: If there are unaddressed errors in data, or if the wrong version of data is used, there will be errors in the resulting analysis or model. In some cases these errors could render the analysis or model completely useless. This is why thorough data preparation is essential for accurate analyses and accurate models. Data preparation will likely always be a major step in the data science process. However, data scientists can speed up the time spent on data prep tasks with a well-documented and curated data catalog, a repository of data cleaning functions, and of course, Python tools and libraries created especially for more efficient data prep. This guide takes a look at some tools and tips to make each step of the data preparation process more efficient. --- > Discover the best AI development tools. Compare model building, training, and deployment tools, and get expert guidance on choosing the right solution. - Published: 2025-01-23 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/guides/ai-development-tools - Resource Types: Guides The demand for AI-powered solutions continues to grow at an incredible rate. From healthcare and finance to retail and manufacturing, organizations are eager to capitalize on artificial intelligence’s outsized business value. However, the journey from concept to deployment of AI applications is fraught with pitfalls and challenges, particularly without the right tools. As AI models become more complex and data volumes expand, building AI solutions has increased in scope, cost, and the technology required. Developing AI solutions requires handling complex datasets, designing and training sophisticated models, and deploying models in production environments that can handle the demands of modern AI applications. Without the appropriate software and compute power, these tasks can become overwhelming and error-prone. This guide aims to illuminate the path for those who are investigating available options for AI development tools. We’ll explore categories of tools, their strengths and weaknesses, and how they fit into different stages of the AI development lifecycle. Common Uses for AI Development ToolsFirst, let’s take a look at the kinds of solutions AI developers are building. In healthcare, AI solutions are instrumental in developing diagnostic systems that can analyze medical images or predict patient outcomes based on complex datasets. Financial institutions create sophisticated fraud detection systems and algorithmic trading platforms that can process vast amounts of market data in real time. In the retail sector, AI powers recommendation engines that personalize customers’ shopping experiences while optimizing inventory management and supply-chain operations. Manufacturing industries use AI development tools to create predictive maintenance systems that... --- > Discover how Python is used for data science and the tools, libraries, and steps used in this process. - Published: 2025-01-23 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/python-for-data-science - Resource Types: Guides Data science is a large and growing field that empowers organizations to make data-driven decisions and improve their operations. Those just getting started with data science will need to learn a programming language like Python to interact with computers, work with data, and build powerful AI and machine learning models. Python’s popularity as a programming language stems from its versatility and ease of use, making it an excellent choice for a range of projects including machine learning and AI. While getting started with Python is relatively straightforward, new data scientists will still need to learn how to set up their environments and install the appropriate libraries for their specific projects. With the right training and guidance, Python’s adaptable nature allows data scientists to thrive across many different domains. Read on to learn more about Python for data science and the most essential tools for data scientists. What Is Data Science? Data science is a critical field that combines different tools and techniques to extract knowledge and insights from structured data (i. e. , organized in a predefined format or schema, such as databases or spreadsheets) and unstructured data (i. e. , text-heavy or multimedia data that lacks a consistent structure, such as emails, videos, and audio recordings). There are also many subdomains of data science, including: Data engineering is a practice that involves collecting and managing data for use within other data science disciplines. Data analytics is a subfield that focuses more on analyzing past performance and enabling data-driven decisions.... --- > Discover the essentials of Python data visualization, including top libraries, practical tips for customization, and techniques for impactful visualizations. - Published: 2025-01-23 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/python-data-visualization - Resource Types: Guides As datasets continue to become larger and more complex, even the most seasoned data experts can struggle to interpret the findings of data science work accurately. Data visualization has become a crucial aspect of data analytics and data science, helping practitioners drive business value with data. Many data science teams leverage data visualization to some degree, yet they still face challenges related to handling large, complex data sets and delivering actionable insights. Python data visualization can help organizations overcome these obstacles and create high-quality visualizations that communicate key messages to stakeholders. The Python ecosystem has many open-source libraries for data visualization — including Matplotlib, Seaborn, Plotly, and Bokeh — to make things even easier for data scientists. In this guide, we’ll discuss common data visualization challenges, the most essential Python libraries, and how to get started with data visualization. Common Challenges with Python Data VisualizationChoosing the right Python library for a particular use case is crucial for overcoming the challenges related to data visualization. Before we examine specific libraries, let’s consider some of the most common data visualization challenges:Handling large and complex data sets can be a problem for some data visualization tools. However, the Pandata open-source data analysis stack curates Python libraries based on certain criteria, including scalability standards. Choosing data visualization libraries from this collection can ensure that large-scale visualizations are responsive and render correctly. Integrating visualizations into existing workflows, such as web apps and reports can be difficult. Some Python libraries like Plotly and Bokeh are ideal... --- > Learn about the most popular Python frameworks available for data science, AI, and web development. - Published: 2025-01-23 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/python-frameworks - Resource Types: Topics Although the Python language is known for its simplicity and readability, much of its value is its extensive package ecosystem. Python makes it easy to install additional open-source packages from a centralized package repository to extend its functionality for specific use cases. It’s worth noting that Python has historically been used for data science, statistical computing, and data analysis, with its adoption for AI and ML being a more recent development. Python frameworks are collections of libraries and tools that handle common tasks and simplify complex functionality. Many popular Python frameworks are now available for machine learning, AI, web development, and other types of projects. By adopting frameworks, Python developers and data scientists can accelerate their workflows and make large projects easier to build. Read on to learn more about the most popular Python frameworks for machine learning, AI, and web development. Popular Python Frameworks by CategoryPython Frameworks for Machine LearningScikit-learnScikit-learn is a free and open-source machine learning library built on NumPy, SciPy, and matplotlib. The library provides simple and efficient tools for predictive analysis, including model fitting, data preprocessing, model selection, model evaluation, and more. This makes Scikit ideal for building and deploying machine learning models in Python. PyTorchPyTorch is a machine learning framework based on the Torch library that was originally developed by Meta AI. It’s one of the most popular libraries for building and deploying deep learning models for computer vision and natural language processing use cases. Along with comprehensive built in features, PyTorch has a rich... --- - Published: 2025-01-22 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/resources/webinar/introduction-to-python-in-excel - Resource Types: Webinar Webinar Description Open-source tools are transforming AI and machine learning (ML) initiatives across industries. From accelerating innovation to addressing pressing security, compliance and scalability concerns, this report—developed by Anaconda in partnership with ETR—offers actionable insights into how enterprises are navigating the opportunities and challenges of open-source AI. Key topics include: Taking your first steps Understanding data types and output modes A brief introduction to pandas DataFrames Custom functions A simplified Python charting experience using the Anaconda Toolbox for Excel Meet Our Speaker Owen Price is a Senior Product Manager of PyExcel at Anaconda and a Microsoft MVP for Excel. With over 20 years of experience in data and analytics, Owen has built a distinguished career, including developing an award-winning visual analytics solution at Kynetec, a global leader in data and insights. He shares his expertise regularly by creating educational video content on Python, SQL, Excel, and other analytics topics across his blog, YouTube channel, and LinkedIn. --- > Learn how Python is used in data analysis and how commonly used Python libraries support the process. - Published: 2025-01-22 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/python-for-data-analysis - Resource Types: Topics Modern businesses collect more data than ever, creating a pressing demand for powerful data analysis tools and skilled data analysts. In fact, data analysis is now a valuable skill in almost every professional industry. The field of data analysis can be overwhelming at first. Still, it’s easier to understand with the right tools — particularly Python. Python is a flexible programming language that’s valuable for learning (and becoming adept at) data analysis. What Is Data Analysis? Data analysis involves collecting, standardizing, transforming, and interpreting data to surface actionable insights that drive business value. Additionally, data analysis is part of data science and is therefore the foundation of many AI and ML workflows. Python is one of the most powerful and widely used tools for data analysis. How Is Python Used in Data Analysis? Python’s versatility and powerful libraries make it an excellent fit for data analysis tasks, from simple statistical tests to complex machine learning models and big data processing. Python is excellent for data analysis because it:Is easy to learn. Is the most popular programming language. Can handle large datasets efficiently. Provides libraries with pre-built functions and tools that simplify data analysis tasks. Is widely supported by open-source libraries and a large user community. Python is well-suited for the following data analysis tasks:Data cleaning and preprocessing: Python’s data manipulation libraries provide essential tools for preparing datasets for analysis. When working with a customer database that contains duplicate entries, missing values, and inconsistent date formats, these libraries can quickly identify... --- > Discover the key differences between Anaconda vs Python in our comprehensive guide. Explore their features, use cases, package management, and more. - Published: 2025-01-15 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/choosing-between-anaconda-vs-python - Resource Types: Topics Anaconda vs. Python: What’s the Difference? When data science teams start a new project, they need to determine which programming languages or tools would be most suitable. Every technology — including Anaconda and Python — has a different learning curve, core capabilities, potential performance, and other factors to consider. Python is one of the most popular programming languages for data science, machine learning, web development, and more. Anaconda is the leading data science platform and an open-source distribution of Python and R. The key distinction is that Python is a programming language, while Anaconda is a distribution of Python tailored for data science. Here’s a quick overview of the differences:Anaconda is a distribution of the Python language with additional tools and packages. Standalone Python is better suited to lightweight projects or web development. Anaconda is ideal for most data science, AI, and machine learning projects. Anaconda supports R and other programming languages besides Python. In this article, we’ll explore Python and Anaconda in more depth so that you can understand their differences, how they relate to each other, and which is right for your projects. What Is Python? Python is a leading general-purpose programming language that was invented in the 1980s and released in 1991 as an easy-to-use option for a variety of projects. Its simplicity, readability, and extensive library ecosystem have led to the widespread adoption of Python for web development, data science, and more. More specifically, Python is a high-level, interpreted language that supports multiple programming paradigms, such... --- > Discover the most popular Python machine learning tools used in data science and analytics. - Published: 2025-01-09 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/guides/python-machine-learning-tools - Resource Types: Guides IntroductionThe demand for machine learning experts has increased in recent years, in large part due to the rise in data collection and the need to derive valuable insights from that data. Python’s simplicity, adaptability, and collection of libraries (including TensorFlow, PyTorch, and scikit-learn) have made it the predominant programming language for machine learning methods. However, those new to machine learning may be overwhelmed by the tooling options available. Because finding the Python libraries best suited for machine learning can be challenging, we’ve created this guide to help you navigate the top tools available, covering essential libraries for data preprocessing, model training, and deployment. Why Are the Right Tools Important for Machine Learning? Not having the right tools for machine learning often means having to manually handle and pre-process large datasets, which can be time-consuming and unreliable. Manual data processing can also introduce delays and possible inaccuracies in training your model. Additionally, without the proper tooling, you may only be able to use simpler models that cannot capture the underlying patterns of your data, since more complex models require specialized libraries for efficient implementation. As datasets and the size/complexity of models grow, scaling machine learning projects becomes more challenging. Version control and experiment tracking tools enable reproducibility; without them, tracking changes and reproducing results is difficult. Without good data visualization tools, extracting insights from your data and effectively communicating findings to stakeholders is challenging. Proper tools also help you satisfy data privacy, security, and compliance standards. Ultimately, trying to do machine... --- - Published: 2025-01-09 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/webinar/top-strategic-technology-trends-for-2025 - Resource Types: Webinar About Compliments of Anaconda, the Gartner Top Strategic Technology Trends for 2025 report is the roadmap CIOs, CTOs, and IT Leaders can bring to their organizations to eliminate productivity, security, and innovation obstacles. Download the Gartner® report to discover: AI imperatives and risks with agentic AI and governance New frontiers of computing– exploring sustainable and quantum computing Human-machine synergy fusing digital and physical worlds Gartner, Top Strategic Technology Trends for 2025, Gene Alvarez, Tom Coshow, et al. , 21 October 2024GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U. S. and internationally and is used herein with permission. All rights reserved. --- > Learn what key features you should look for in every type of MLOps tool and the top tools that every organization should consider. - Published: 2025-01-02 - Modified: 2025-07-23 - URL: https://10.2.107.56:8443/guides/the-top-mlops-tools - Resource Types: Guides IntroductionOrganizations that struggle with machine learning workflows, model deployment issues, lack of visibility into model performance, and other challenges are increasingly turning to new machine learning engineering practices. Machine learning operations (MLOps) is an important function that aims to use automation and standardization to get high-quality models into production faster, as well as facilitate reproducibility and iteration. A typical MLOps pipeline involves ingesting data and using feature engineering to uncover relevant input variables for machine learning models to use. Afterward, models can be trained and deployed into production. Once a model is operational, continuous monitoring and retraining are used to maintain and improve performance over time. In this guide, we’ll explore how MLOps tools can simplify and automate nearly every aspect of an MLOps pipeline. We’ll also cover the different categories of MLOps tools, highlight the top solutions in each category, and provide a framework for evaluating and choosing the right tools for your organization. By streamlining these processes, MLOps tools empower teams to build and deploy AI solutions faster and more reliably, Understanding MLOps ToolsMLOps tools can be categorized based on their main functions within the MLOps pipeline. By understanding these categories, organizations can choose the tools that align with their unique needs. MLOps Platform: Provides a comprehensive solution that includes capabilities for every aspect of the MLOps pipeline, from data ingestion through to model deployment and monitoring. Experiment Tracking and Model Management: Manages machine learning experiments and different model versions to ensure they can be shared and reproduced.... --- > Explore the complexities of open-source security, including risks, benefits, and strategies to safeguard your software supply chain against vulnerabilities. - Published: 2024-12-16 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/open-source-security - Resource Types: Guides IntroductionOpen-source software has become an integral part of modern software and application development, offering numerous benefits such as transparency, collaboration, and access to cutting-edge tools. Its use is widespread in software development, data science, and artificial intelligence. However, this broad adoption requires strong security measures. Over the past few decades, open-source software has transitioned from a niche approach to software development to a mainstream model embraced by individuals, businesses, and governments. This rapid adoption can be attributed to several factors. The collaborative nature of open-source development has accelerated innovation and software improvement. The availability of high-quality, free tools has lowered barriers to entry for developers and organizations. Additionally, the flexibility and customizability of open-source solutions have made them attractive for a wide range of applications, from operating systems and web servers to machine learning libraries and data analysis tools. Today, open-source components can be found in nearly every type of software, from mobile apps to enterprise systems, cloud infrastructures, and even in highly regulated industries like finance and healthcare. The vast majority (96%) of applications used by major industries include open-source software, according to MIT News. This guide examines the concept of open-source security and its significance in today’s digital market. It provides guidance on best practices for organizations leveraging open-source software, explores the unique challenges posed by open-source software, and discusses the tools and strategies available to mitigate risks effectively. Understanding these concepts can help you utilize the full potential of open-source software while maintaining protection against threats. What... --- > Discover the best Python libraries for data science, machine learning, and more. Discover top libraries, their use cases, and practical examples to get started. - Published: 2024-11-06 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/best-python-libraries - Resource Types: Topics IntroductionPython has grown to become one of the most popular programming languages because of its versatility for a wide range of tasks, from data manipulation and analysis to machine learning and web development. Along with its flexibility and simple syntax, Python has a large ecosystem of libraries and frameworks that simplify complex tasks and facilitate rapid development. Although the Python Standard Library provides a lot of native functionality out of the box, it’s easy to install additional open-source packages to extend Python’s capabilities. This means data scientists can choose from hundreds of libraries from a centralized package repository to make their workflows more efficient. In this article, we’ll discuss the top Python libraries for different use cases and how to choose the right ones for your projects. Understanding Your NeedsSince there are so many open-source Python packages available, it’s important to evaluate your project requirements before selecting a library. The features, ease of use, and scalability of a particular library can impact the success of your data science or machine learning project. Here are some key questions to ask when choosing a library:What type of data will I be working with? Numerical, text, images? What tasks do I need to accomplish? Data analysis, visualization, machine learning? What is my skill level with Python and specific libraries? Will the library meet the performance requirements of my project? Is this the latest version of the library? What are the dependencies for the latest version of the library? Popular Python Libraries by CategoryPython... --- > The 2024 data science platform buyer's guide aims to help readers understand the features, benefits, and key considerations of the top data science platforms. - Published: 2024-10-18 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/data-science-platform-buyers-guide - Resource Types: Guides IntroductionToday, there are a number of data science platforms to choose from with new options emerging every year as the field continues to evolve. This can make it difficult for organizations to choose the right solution for their specific use cases. In fact, many organizations select a data science platform only to face onboarding challenges, integration issues, scalability and performance limitations, and other obstacles. That’s why we’ve put together this guide to help you navigate the complex data science ecosystem and choose a platform that brings the most value to your organization. Read on to uncover the key features, benefits, and factors to consider when choosing a data science platform. Understanding Data Science PlatformsA data science platform integrates multiple tools and libraries into a single solution for various use cases. These platforms often include package managers, development environments, collaboration tools, and other features to support the entire data science and AI lifecycle. The key components of a data science platform are:Security and compliance: Security measures like access controls, data encryption, and audit trails can protect data and ensure regulatory compliance. Data ingestion and preparation: Capabilities for collecting data from different sources like databases and APIs as well as preparing this raw data for analysis by handling missing values, removing duplicates, and transforming the data into a suitable format. Data exploration and visualization: Features for interacting with data, performing exploratory analysis, and creating visual representations of any insights generated. Model building, training, and deployment: Tools and integrations for creating and running... --- > Learn what key features you should look for in every type of data science tool and the top tools that every organization should consider. - Published: 2024-10-18 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/data-science-tools - Resource Types: Guides IntroductionData science has undergone a significant transformation in recent years, driven by the emergence of sophisticated tools — particularly machine learning, artificial intelligence, and open-source software. What was once an extremely specialized domain requiring access to powerful, expensive computing resources has become more accessible to a broad spectrum of users. Additionally, practitioners no longer require extensive technical knowledge (as was the case in the past) thanks to the rise of pre-built models and tools. Open-source software has made advanced algorithms and frameworks freely available. The rise of cloud computing has eliminated the need for massive upfront investments in hardware. Advancements in machine learning and AI have automated many complex analytical tasks, allowing data scientists to focus on higher-level problem-solving and interpretation. As a result, the tooling options available to modern data scientists have increased considerably. From programming languages like Python and R to specialized libraries for data manipulation, visualization, and machine learning, the choices are extensive. This variety in tooling can be exciting, but also overwhelming. Many users face challenges in this selection process, from limited knowledge of available tools to the need for solutions that address project-specific requirements. This guide aims to demystify the process of selecting data science software by breaking down the key features and benefits of various types of tools. We’ll explore the different categories of data science tools, highlight the top solutions in each category, and provide a framework for evaluating and choosing the right tools for your organization. Understanding Data Science ToolsData science tools... --- > Explore the key differences between deep learning and machine learning. Understand their applications to choose the right approach for your AI projects. - Published: 2024-10-18 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/deep-learning-vs-machine-learning - Resource Types: Topics Business interest in artificial intelligence (AI) has reached a fever pitch. As a result, subsets of AI — machine learning (ML) and deep learning (DL) — are gaining significant attention. The differences between these two fields are subtle, but it’s important to understand them to maximize business value for your organization. This article will compare and contrast ML and DL, address common questions and misconceptions, and consider how machine learning and deep learning models can be essential tools for businesses that rely heavily on technology and software innovation. What is Machine Learning? Machine learning is a subset of AI that focuses on developing algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience. At its core, ML is about creating programs that learn from data and make decisions based on it. Machine learning algorithms come in various forms, such as linear regression, decision trees, and support vector machines. For example, a linear regression model might be used to predict house prices based on features like square footage, number of bedrooms, and location. Decision trees could be employed to classify emails as spam or not spam based on their content and metadata. Machine learning can be broadly categorized into supervised and unsupervised learning paradigms. In supervised learning, the algorithm is trained on labeled data, where the desired output is known. Unsupervised learning, on the other hand, works with unlabeled data, trying to find patterns or structures within the dataset. Two important aspects of... --- > Understanding the overlap and differences between data science and machine learning helps you leverage each technique effectively for your organization’s needs. - Published: 2024-10-18 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/topics/data-science-vs-machine-learning - Resource Types: Topics IntroductionTwo terms that often arise in discussions around leveraging data are data science and machine learning. While these concepts are closely related (and sometimes mistaken to be the same), each has distinct characteristics and applications. Understanding the ways in which data science and machine learning overlap and differ can help you determine how to best leverage the strengths of each technique for your organization’s unique needs. What is Data Science? Data science is a multidisciplinary field that combines various tools and techniques to extract knowledge and insights from structured data (i. e. , organized in a predefined format or schema, such as databases or spreadsheets) and unstructured data (i. e. , text-heavy or multimedia data that lacks a consistent structure, such as emails, videos, and audio recordings). It’s a holistic approach to data analysis that goes beyond simple statistical calculations or data visualization. At a basic level, data science is about solving complex problems using data. It involves a wide range of activities, including:Data collection and preprocessing: This involves gathering data from different sources, cleaning it, and preparing it for analysis. For example, a retail company might collect customer purchase history, website clickstream data, and demographic information. Exploratory data analysis: Data scientists often take a “first pass” at examining data to discover patterns, identify anomalies, and form hypotheses. For example, this exploratory analysis might reveal information about the distribution of customer ages or the relationship between product categories and sales. Statistical modeling: This involves using statistical techniques to test hypotheses... --- > Learn what to consider when choosing an enterprise platform for building and deploying powerful, secure Python, AI, machine learning, and data science solutions. - Published: 2024-10-03 - Modified: 2025-07-14 - URL: https://10.2.107.56:8443/guides/the-ultimate-guide-to-open-source-security-with-python-and-r - Resource Types: Guides Open-source software (OSS) has emerged as a powerful force, revolutionizing the way organizations approach data science and machine learning development, collaboration, and innovation. With a wealth of benefits including transparency, cost-effectiveness, and a vast community of contributors, open-source software has garnered widespread adoption across industries. However, open-source security brings challenges and threats every day that can separate the victors from the vanquished in a new era of security threats. Navigating this new game requires a strategic approach and a willingness to evolve. As the adoption of open-source grows, organizations must comprehend the importance of adaptable strategies that leverage the power of open-source technology, apply proprietary solutions with care, and safeguard against potential risks. As you make decisions about the platforms and tools to include in your data science and machine learning technology stack, it’s important to consider how open-source security is managed by the authors and maintainers of the tools you choose to use. We’ve created this guide to be a handy reference about open-source security and critical considerations to ensure a secure software supply chain, and best practices for open-source security with Python and R. If you find this guide helpful, please bookmark this page and share it with colleagues. IntroductionIn this guide, we will cover open-source security for Python and R. We will define open-source software, open-source software tools, and open-source security. We will consider the security advantages and challenges of using open-source software and explore approaches for security when you are working with open-source repositories, libraries, packages,... --- - Published: 2023-02-15 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/report/2022-state-of-data-science/ - Resource Types: Report 2022 State of Data Science This year, we conducted our State of Data Science survey to gather demographic information about our community, ascertain how that community works, and collect insights into big questions and trends that are top of mind within the community. 3,493 individuals from 133 countries and regions took part in the online survey conducted from April 25th to May 14th. *Our resulting 2022 State of Data Science report looks at actionable issues within the data science, machine learning, and artificial intelligence industries, like open-source security, the talent dilemma, ethics and bias, and more. *In the spirit of democratizing data, we are pleased to make the raw data from our 2022 State of Data Science survey available to the public via Anaconda Nucleus. Get the Report --- - Published: 2023-01-11 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/data-engineering-as-a-scientific-tool - Resource Types: Podcast Show Notes In this episode, host Peter Wang is joined by Dr. Patrick Kavanagh, an astrophysicist and software developer at the Dublin Institute for Advanced Studies. Patrick works on the James Webb Space Telescope (JWST), helping to write code that allows scientists to interpret the raw data they receive from space. Patrick talks to Peter about cleaning telescope data sets to make them more scientifically useful, and more. Patrick’s team working on the Mid-Infrared Instrument on the JWST writes software in Python to help deliver science-ready data to astronomers and astrophysicists. Patrick’s work facilitates more precise study of distant stars and galaxies in a way that fosters public trust. Check out these relevant resources: Dr. Patrick Kavanagh – EuroPython Python and James Webb Judy Schmidt (citizen scientist) If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast shows. --- - Published: 2022-12-28 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/optimizing-python-for-speed-and-compatibility - Resource Types: Podcast Show Notes In the penultimate episode of season one, host Peter Wang and Carl Meyer, Software Engineer at Instagram (owned by Meta), discuss considerations around making Python faster while maximizing compatibility and performance. Several years ago, Carl and his team started working on a project called Cinder in an effort to improve CPU efficiency across Meta’s servers by “ things at the level of Python runtime. ” While initially meant to serve as a stop gap, Cinder yielded impressive wins that transformed it into a premier and ongoing project at Instagram. In addition to Cinder, Peter and Carl discuss: Carl’s experiences with various programming languages like TI-Basic, Perl, and PHP Challenges around innovating on an established language with 30+ years of history The potential evolution of Python use cases and best practices And more! If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast episodes. --- - Published: 2022-12-14 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/climate-science-scientific-computing-and-data-accessibility - Resource Types: Podcast Show Notes This episode’s conversation between host Peter Wang and Ryan Abernathey, Associate Professor at Columbia University in the City of New York, explores climate science, scientific computing, data accessibility, and more. Topics that Peter and Ryan cover include:Cloud computingOpen data and collaborationClimate science and the private sectorOpen-source projects like Pangeo Forge and XarrayClimate data is sometimes restricted in the way it flows between interested parties; the growth of private industry around data storage and dissemination has put up barriers to entry that can limit access to valuable systems and data. This is especially troubling to Ryan because these barriers often exclude some of the people who are most affected by climate change. He feels that usable information can and should be made accessible without undermining private interests. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast shows. --- - Published: 2022-11-30 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/shaping-best-practices-for-monitoring-ml-models - Resource Types: Podcast Show Notes In this episode, host Peter Wang is joined by Elena Samuylova, CEO and Co-Founder of Evidently AI. Peter and Elena discuss how Evidently AI’s open-source tooling is helping users monitor machine learning (ML) models, and why that’s important. Elena has found that Evidently AI’s open-source approach is attractive to data scientists and ML engineers who are ramping up model maintenance, retraining, and monitoring efforts. Peter and Elena also touch on:On-premises versus cloud-based deploymentML model monitoring best practicesThe value of pipeline testingAnd more! You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast shows. --- - Published: 2022-11-16 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/unifying-and-accelerating-data-science-ml-and-advanced-analytics-workflows - Resource Types: Podcast Show Notes anaconda-snowflake-and-advanced-analytics-podcast-episode6-transcriptIn this episode, host Peter Wang speaks with Torsten Grabs, Director of Product Management at Snowflake, about how Snowflake solutions support professionals in data science, machine learning, and advanced analytics. Torsten has worked with data throughout his entire career. At Snowflake, he focuses on Snowflake’s data lake, data pipelines, and data science workloads, as well as Snowflake’s developer and partner ecosystem. Thanks to the broader language compatibilities of Snowflake and its Snowpark library, data engineering is becoming more accessible beyond the SQL community. Torsten and Snowflake continue to work to unify and accelerate data workflows. Learn more about Snowpark for Python, now generally available, and get started with the Snowpark Developer Guide for Python. Then learn how Snowflake customers like Allegis Group are leveraging Snowpark for Python. Access Anaconda’s State of Data Science report, referenced by Peter, here. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast episodes. --- - Published: 2022-11-02 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/autopoiesis-in-systems-of-people-and-machines - Resource Types: Podcast Show Notes In “Autopoiesis in Systems of People and Machines,” Peter Wang welcomes Paco Nathan. Paco is a Managing Partner at Derwen, Inc. , a company that offers enterprise customers full-stack engineering for AI applications at scale, with an emphasis on open-source integrations. Paco forged a career in artificial intelligence when many people were skeptical of it and now boasts over 40 years of computer science experience. Peter and Paco discuss histories and frameworks that are impacting today’s systems of people and machines. Paco touches on corporate law and how long ago, the concept of insurance allowed for the externalization of risk and corresponding enablement of capital ventures. Paco goes on to talk about autopoiesis, the Chilean Project Cybersyn and the significance of groupware, and the core of human intelligence. Peter and Paco also discuss the increasing complexity of today’s world in which less and less is linear, which requires improved cognition for survival, and the cybernetic future. Resources: “A Brief History Of Reinsurance” (David M. Holland) Santa Clara County v. Southern Pacific Railroad Co. , 118 U. S. 394 (1886) “Law as an Autopoietic System” (Gunther Teubner) Autopoiesis and Cognition: The Realization of the Living (Humberto Maturana and Francisco Varela) Project Cybersyn “Understanding Computers and Cognition” (Terry Winograd and Fernando Flores) Macy Conferences, Norbert Wiener “What the Frog’s Eye Tells the Frog’s Brain” (J. Y. Lettvin et al. ) Social Systems (Niklas Luhmann) Dubberly Design (Paul Pangaro) (When Paco references Donoho Design, he means Dubberly Design. ) René Thom... --- - Published: 2022-10-19 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/from-enthusiastic-user-to-pandas-maintainer - Resource Types: Podcast Show Notes On this episode of Numerically Speaking: The Anaconda Podcast, host Peter Wang welcomes pandas maintainer Jeff Reback, Managing Director at Two Sigma. Jeff began his career on Wall Street in the 1990’s and used Perl for a long time. He developed an interest in Python in the 2000’s. He was then quickly drawn to pandas and began to spend his hour-long ferry commutes contributing to its open-source code. His contributions over the years have been significant, to say the least. When it comes to open source, says Peter, “my flame isn’t diminished by lighting your candle. ” Cloning a copy of pandas, for example, does not make the original copy any less valuable. In fact, source code actually increases in value as it circulates. Until recently, only volunteers worked on pandas—but as of 2022, three full-time maintainers are paid to contribute, review code, and triage issues. Jeff’s advice for anybody interested in contributing to open source? Find a community and just help out. Click here to check out “Two Sigma Presents Pandas at a Crossroads the Past Present and Future with Jeff Reback” on YouTube. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast shows. --- - Published: 2022-10-05 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/a-specialized-approach-to-hardware - Resource Types: Podcast Show Notes End users who are not schooled in hardware can often default to, “just give me something that works. ” David Liu, Staff AI Engineer, Strategy & Vision for Data Science and AI Products at Intel, understands this thinking but also believes that end users can be educated on the advantages of configuring their computer hardware to suit their specific needs. David advocates for using the right hardware for a given task—and that may mean different configurations and/or different machines for different tasks, rather than a one-size-fits-all solution. David and host Peter Wang also discuss: The need for more education and resources around hardware performance Intel’s Optane technology and the possibilities it creates Click here to visit David’s YouTube channel. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast shows. --- - Published: 2022-09-21 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/resources/podcast/human-in-the-loop - Resource Types: Podcast Show Notes Machine learning (ML) has reached an exciting phase of development, a phase that Vicki Boykis, Senior ML Engineer at Duo Security* has characterized as the “steam-powered days. ” In this episode of Numerically Speaking: The Anaconda Podcast, Vicki talks about the state of the industry and where she sees things heading. Vicki’s discussion with host Peter Wang covers: the interplay between software engineering and ML the human element of the development lifecycle (and the lack thereof in social media) operationalization and the rise of microservices Resources: Click here to visit Vicki’s blog. Click here to purchase The Presentation of Self in Everyday Life by Erving Goffman, referenced by Vicki. Click here to purchase Broad Band: The Untold Story of the Women Who Made the Internet, also referenced by Vicki. Click here to listen to the Jim Rutt/Rob Malda (Slashdot) podcast episode referenced by Peter. Check out the P2 website here. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast shows. *At the time of the interview, Vicki Boykis was an ML Engineer working on Tumblr at Automattic. --- - Published: 2022-09-07 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/resources/podcast/software-venture-capital-and-the-future-of-work - Resource Types: Podcast Show Notes While today’s software may seem magical compared to that of previous generations, it still takes multiple software iterations to fold in new fundamental technologies. Joining us for this episode is James Cham, Partner at Bloomberg Beta. Bloomberg Beta runs several seed-stage investment funds, with a particular interest in low-code/no-code/WebAssembly startups. In this episode, James and host Peter Wang discuss:why it’s important to be humble when looking towards the future of softwarewhy venture capitalists (VCs) shouldn’t be considered “Yodas” who can fix every problemwhat James is looking for when it comes to investing in a businessAfter listening to this episode, you may enjoy reading “Selling Wine Without Bottles” by John Perry Barlow, referenced by Peter during the discussion, and “Seeing Like a Finite State Machine” and “Street–Level Algorithms: A Theory at the Gaps Between Policy and Decisions,” referenced by James. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. Listen to other Anaconda Podcast episodes --- - Published: 2022-07-18 - Modified: 2025-04-23 - URL: https://10.2.107.56:8443/resources/podcast/introducing-numerically-speaking-the-anaconda-podcast - Resource Types: Podcast Show Notes In this introductory episode of Numerically Speaking: The Anaconda Podcast, Anaconda CEO Peter Wang provides an overview of what to expect from the show. Peter will be exploring a variety of topics within the dynamic world of data science, including quantitative computing, business, and entrepreneurship. Guests will include top data science experts as well as creators of cutting-edge open-source tools. Whether you want to learn about AI, grow your data science career, or simply better understand the numbers and computers that shape our world, this podcast is for you. We’re excited to bring you insights about data science and the people that make it happen. Be sure to subscribe to stay up to date with new episodes. This episode is brought to you by Anaconda, the world’s most popular data science platform. We are committed to increasing data literacy and providing data science technology for a better world. Anaconda is the best way to get started with, deploy, and secure Python data science software. You can find a human-verified transcript of this episode here. If you enjoyed today’s show, please leave a 5-star review. For more information visit anaconda. com/podcast. Peter Wang on Twitter Anaconda, Inc. on Twitter Python --- - Published: 2021-07-07 - Modified: 2025-05-01 - URL: https://10.2.107.56:8443/resources/whitepaper/state-of-data-science-2021 - Resource Types: Report State of Data Science 2021 The 2021 State of Data Science report looks at how data science as a field is growing, the overall trends in adoption from commercial environments and academic institutions, and what students can do to prepare for the future. For this year’s online survey, we received more than 4,200 responses from individuals using data science and machine learning tools in more than 140 countries. THE PANDEMIC’S INFLUENCE ON DATA SCIENCEDid the COVID-19 pandemic impact your organization’s investment in data science? COVID-19 had a trickle-down effect that impacted virtually every industry – from healthcare to government, financial institutions, and more; they all needed to find ways to act quickly on data and find solutions to new problems. Additionally, when asked how involved their role is in business decisions, 14% of respondents said “all” decisions rely on insights interpreted by them or their team, and 39% said “many” business decisions rely on them. While there is still work needed to ensure we bring data scientists into the fold, it’s encouraging to see their value is recognized in organizations and might be why the field avoided a sharp decrease in investment. DATA JOBS AND THE FUTURE OF WORKWhat is your sentiment toward automation or AutoML, the process of automating tasks involved in applying machine learning to real-world problems, in data science? A common theme in the news today is that automation is taking over and will eventually replace human workers. However, results show that automation is welcomed in the... --- - Published: 2020-06-30 - Modified: 2025-05-02 - URL: https://10.2.107.56:8443/resources/whitepaper/state-of-data-science-2020 - Resource Types: Report 2020 State of Data Science The State of Data Science 2020 Moving from hype toward maturityThe good news is, the hype around data science and machine learning is giving way to reality. But the bad news is that we’ve got a long way to go before we achieve maturity. Enterprises, academics, and data professionals have work to do before the discipline can really deliver on its potential for business and society. Our 2020 State of Data Science report takes a look. The face of data scienceFor this year’s survey we recruited individuals using data science and machine learning tools, as well as those assisting data scientists with their work, and received a total of 2,360 responses from over 100 countries. Respondents included data scientists, researchers, developers, analysts, data engineers, business managers, and more, showing that the disciplines and skill sets that power the field of data science remain as diverse as ever. While enterprises have embraced open-source tools in a wide variety of functions, their security practices don’t always keep pace. A concerning 30% of respondents who have knowledge of their company’s security practices stated that their organization does not have any mechanism in place to secure open-source tools used for data science and machine learning. HOW DO YOU ENSURE THAT OPEN-SOURCE PACKAGES USED FOR DATA SCIENCE AND MACHINE LEARNING MEET ENTERPRISE SECURITY STANDARDS Although COVID-19 has brought uncertainty to the employment landscape, enterprises should still pay attention to job satisfaction among data scientists. One-third of data professionals reported they... --- --- ## Partners > Lenovo, the #1 PC company in the world, powered by Intel Xeon processors on Lenovo Workstations, offers an optimal solution to support Anaconda software for AI deployments and software development. - Published: 2025-07-17 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/partners/directory/lenovo Leverage Navigator’s AI capabilities on Lenovo’s high-performance workstations, powered by Intel processors. Lenovo™ has partnered with Anaconda to empower Lenovo’s high-performance data science workstations. This collaboration merges the heritage and leadership of Lenovo’s ThinkStation™ and ThinkPad™ workstation product, powered by Intel® processors, with Anaconda’s expertise in open-source leadership, security, and reliability. In today’s rapidly evolving AI landscape, businesses and data scientists face unprecedented opportunities. Much AI innovation stems from open-source software and cloud-based solutions, with Python as the leading language for AI applications. However, concerns over data security, privacy, and costs have prompted a re-evaluation of AI strategies. With Intel-powered Lenovo workstations architected with the latest generations of professional NVIDIA® GPUs built for LLM fine-tuning and Navigator’s capabilities, businesses can now gain access to top-tier hardware and enterprise-grade software support, within a manageable investment framework. Lenovo’s new generation of workstations offer exceptional AI performance, flexibility, and productivity enhancements. Lenovo’s workstation lineup is designed for nearly every AI workflow, industry or vertical, size and price point, from single CPU mobile workstations to the most powerful dual CPU and four GPU configurations for advanced AI workflows. Lenovo workstations with Navigator offer protected “sandbox” environments for complex AI solution development and deployment. “With Lenovo’s trusted workstation leadership, Intel’s powerful processors, and Anaconda’s trusted leadership in open-source software support and reliability, the partnership is a perfect match,” according to Rob Herman, VP and GM of Lenovo’s Workstation and Client AI Group. Navigator is now available for download on current and future generation Lenovo workstations.... --- > You can now confidently access Anaconda's curated library of open-source packages within Microsoft Cloud-hosted products and services like Azure Machine Learning. - Published: 2025-07-03 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/partners/azure AUSTIN, Texas, Oct. 21 2021 -- Today, Anaconda, Inc. announced a collaboration with Microsoft to enable customers to confidently access Anaconda’s curated library of open-source packages within Microsoft Cloud-hosted products and services, including Azure services like Azure Machine Learning, as well as GitHub services such as GitHub Codespaces and GitHub Actions, without the requirement of a separate license1. “We are committed to making it easy to use Anaconda everywhere and that includes inside Microsoft’s cloud,” said Peter Wang, CEO and co-founder of Anaconda. “By combining Anaconda’s package dependency manager and curated open-source repository with Microsoft’s cloud products, data scientists and developers can use tools they know and trust with the peace of mind that they do not have to worry about additional licensing. ”Organizations that capitalize on the innovation from thousands of makers and contributors in the open-source community have a competitive advantage and are able to accelerate projects that would typically take years. This collaboration expands the availability of key open-source data science tools across platforms and sets enterprises up for greater success by making it simpler for users to focus on end results. “Open-source packages have been the biggest enabler for data science we’ve seen in recent years,” said Mark Russinovich, chief technology officer and technical fellow, Microsoft Azure. “Being able to offer a set of trusted tools from Anaconda will empower our customers through every stage of the data science journey on Microsoft Azure. ”In addition, Anaconda supports establishing an industry-standard software bill of materials (SBOM) format... --- > With IBM Cloud Pak for Data and IBM watsonx.ai, you can automate AI lifecycles with comprehensive, curated open-source libraries and tools provided by Anaconda. - Published: 2025-07-03 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/partners/ibm IBM and Anaconda have expanded their collaboration to bring Anaconda’s secure open-source Python repository to IBM® Cloud Pak for Data and IBM watsonx. ai. Now, organizations can automate AI lifecycles with comprehensive, managed and curated open-source libraries and tools provided by Anaconda. IBM Cloud Pak for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud native by design, you can build, deploy and manage any model—whether it’s based on open source, IBM tools or other pre-built services in this unified, secure environment with AI governance. IBM watsonx. ai is part of the IBM watsonx™ AI and data platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx. ai, you can train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data. With Anaconda Repository for IBM Cloud Pak for Data and for IBM watsonx. ai, you can easily put open-source and proprietary packages in the hands of your team so they can be productive and efficient, while maintaining control of the project and having the peace of mind of knowing the who, what, when, and where of packages and artifacts in your environment. Whether it’s generative AI applications or advanced statistical modeling, with Anaconda’s secure repository, both individuals and... --- > Bringing Anaconda to Linux on IBM Z and LinuxONE expands the availability of key open-source data science tools across platforms and improves practitioners' experience. - Published: 2025-07-03 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/partners/ibm-z Anaconda and IBM have teamed up to bring Anaconda to Linux on IBM Z and LinuxONE to expand the availability of key open-source data science tools across platforms and improve the experience for practitioners everywhere. With this collaboration, existing IBM Z and LinuxONE users can now deploy their data science workflows while using the tools and frameworks they’re used to, such as scikit-learn, PyTorch, and more. This gives data scientists, data engineers, and application developers a platform for their most sensitive and critical machine learning workloads, and the ability to run data science where their mission-critical data and processes already live, spanning training and production deployments across Linux on Z, LinuxONE, and through zCX (z/OS Container Extensions) for z/OS. Read the Solution Brief --- > Anaconda and Oracle Cloud Infrastructure bring you secure open-source Python and R tools and packages by embeddingAnaconda’s repository across OCI AI and ML services. - Published: 2025-07-03 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/partners/oci Anaconda has collaborated with Oracle Cloud Infrastructure (OCI) to offer secure open-source Python and R tools and packages by embedding and enabling Anaconda’s repository across OCI Artificial Intelligence and Machine Learning Services. Customers have access to Anaconda services directly from within OCI without a separate enterprise license. “We are committed to helping enterprises secure their open-source pipelines through the ability to use Anaconda anywhere, and that includes inside the Oracle Cloud,” said Peter Wang, CEO and co-founder of Anaconda. “By combining Anaconda’s package dependency manager and curated open-source repository with OCI’s products, data scientists and developers can seamlessly collaborate using the open-source Python tools they know and trust – while helping meet enterprise IT governance requirements. ”Python has become the most popular programming language in the data science ecosystem, and for good reason; it is a widely-accessible language that facilitates a wide variety of programming-driven tasks. Because the velocity of innovation powered by the open-source community outpaces any single technology vendor, more and more organizations are adopting open-source Python for enterprise use. “Oracle’s partnership to provide data scientists with seamless access to Anaconda not only delivers high-performance machine learning, but also helps ensure strong enterprise governance and security,” said Elad Ziklik, vice president, AI Services, Oracle. “With security built into the core OCI experience, plus the security of Anaconda’s curated repository, data scientists can use their favorite open-source tools to build, train, and deploy models. ” Get Details for OCI Users --- > Anaconda for Linux on the aarch64 (arm64) platform optimized for AWS’s Graviton2 processors enables end-to-end data science in the cloud, from development to production. - Published: 2025-03-27 - Modified: 2025-07-03 - URL: https://10.2.107.56:8443/partners/aws Anaconda for Linux on the aarch64 (arm64) platform optimized for Amazon Web Services’ Graviton2 processors enables end-to-end data science in the cloud, including development, training, testing and production. Data scientists can leverage the power and savings of Graviton2 while still using favorite tools and frameworks such as Conda, SciKit-Learn, and XGBoost. Graviton2 processors are custom built by AWS using 64-bit Arm Neoverse cores. Graviton2-based instances offer up to 40% better price performance over comparable current-generation x86-based instances for a wide variety of workloads, including high-performance computing and CPU-based machine learning inference. As with our current linux-64 (x86-based) conda packages, linux-aarch64 packages are supported in Anaconda’s “defaults” channel and will be regularly updated as the open-source community publishes new releases. Download Miniconda, Anaconda Distribution¹, or Anaconda Professional to start using linux-aarch64 packages on AWS Graviton2 today. For more information, please visit our docs, Installing on AWS Graviton2 (arm64). Subject to Anaconda’s Terms of Service. --- --- ## Events - Published: 2025-08-27 - Modified: 2025-08-28 - URL: https://stage.anaconda.com/event/meet-anaconda-at-databricks-world-tour-los-angeles-2025 Meet Anaconda in LA! Databricks World Tour Sept 23 2025 Stop by our booth and ask to see a demo! Book a meeting with our experts to find out more about how your organization can innovate faster,securely with Anaconda for open source AI at scale. Meet with us Join us at Anaconda’s Booth! Watch Watch a demo designed with Databricks integration to evaluate ML models Learn Learn about rapid deployment: from secure setup to running model in less than one day Meet Meet our team for demos and expert insights on Python for AI Enterprise-Ready Open Source for Data+AI Join us at Databricks World Tour LA! Discover how Anaconda's native integration with Databricks delivers seamless access to trusted, secure Python packages within the Data Intelligence Platform. Learn how our partnership empowers faster AI innovation while reducing security risks and time-to-market. Book a Meeting Additional Information Anaconda & Databricks One-Pager Enterprise-Ready Open Source for Data and AI. Powered by Anaconda, Delivered in Databricks. Download Now Press Release: Anaconda Partners with Databricks Anaconda partners with Databricks to deliver secure, enterprise-grade Python AI development Learn More Enterprise AI’s Open-Source Challenge Anaconda-Databricks integration delivers secure, enterprise-grade Python AI development at scale Read our Blog Anaconda AI Platform: Code Once, Use Anywhere Experience the only unified AI platform that combines trusted distribution, simplified workflows, and enterprise-grade governance to boost practitioner productivity by 80% Learn More Meet Us at Future Events Join Anaconda at upcoming industry events! Meet our experts and discover how to accelerate your... --- - Published: 2025-08-05 - Modified: 2025-08-08 - URL: https://stage.anaconda.com/event/anaconda-at-ai4-2025 Anaconda at Ai4 2025 Come see us at Booth #523Ready to transform your AI strategy? Let's discuss your AI challenges. Meet with us at Ai4 and Starbucks ($25 gift card) is on us! Book a Meeting Monday, August 11 • 4:25 - 4:45 PM • Room 150 (Level 1) Don’t Miss Peter Wang’s Session --- > Discover how Anaconda empowers enterprises, practitioners, and partners to simplify AI and data science on CUDA with GPU-accelerated workflows. - Published: 2025-07-30 - Modified: 2025-08-08 - URL: https://stage.anaconda.com/event/scipy-conference Anaconda at SciPy 2025 Visit the Anaconda Booth Conference Wrap-Up Another fantastic event with the Python community is in the books! This year delivered breakthrough presentations, hands-on learning, and inspiring conversations that showcase the future of scientific computing. The conference buzzed with excitement around Siu Kwan Lam's groundbreaking work on "Numba v2: Towards a SuperOptimizing Python Compiler. " Attendees were captivated by the next-generation compiler's composable term rewriting rules and automatic GPU acceleration capabilities. The momentum carried into sprint sessions where teams collaborated on executable Jupyter notebooks for the Numba v2 book project. Jim Kitchen and Microsoft's Sarah Kaiser brought crowds with their practical tutorial on developing Pythonic spreadsheets. Their session demonstrated the powerful intersection of accessibility and analytics, showing how Python integration transforms traditional Excel workflows. Meanwhile, the prefix. dev team tackled one of science's biggest computational challenges through their Pixi presentations. Their half-day tutorial on reproducible machine learning workflows provided scientists with concrete solutions for consistent environments across different computing platforms, featuring robust project-based package management. Our booth became a hub for remarkable stories from around the globe. We heard from educators creatively distributing Anaconda on USB drives in Ghana and witnessed first-time Python users discovering the language's potential. These conversations highlighted Python's expanding reach and the passionate stories unlocked with its advancements. Anaconda Speaker Spotlight Monday, July 7 | 1:30 PM - 5:30 PM | Room 318 Develop Pythonic spreadsheets running Python in and out of the grid Tutorial with Jim Kitchen, Sr. Software Engineer at Anaconda... --- > RSVP for the Anaconda Executive AI Roundtable on July 31, 2025. Join our Chief AI Officer and Managing Director at Insight Partners in New York City to discuss AI business impact strategies. - Published: 2025-07-17 - Modified: 2025-07-25 - URL: https://10.2.107.56:8443/events/executive-ai-roundtable Event Anaconda Executive AI Roundtable Thursday, July 31, 2025 | 1:00 PM - 3:00 PM STK Steakhouse, Midtown Manhattan (1114 6th Ave) About Join fellow experts navigating the challenges of transforming AI pilots into game-changing business results for an exclusive lunch hosted by Anaconda. Why Attend: Intimate executive roundtable discussion with Peter Wang (Co-Founder & Chief AI Officer, Anaconda) and George Mathew (Managing Director, Insight) Strategic insights on what's working in enterprise AI today Learn unified platform architecture and risk management approaches Discover the path to measurable AI ROI Network with executives serious about scaling AI responsibly Space is intentionally limited for meaningful dialogue. Register to secure your spot by 6:00 PM EST on Wednesday, July 23, 2025. Meet Our Speakers Peter Wang is the Chief AI and Innovation Officer and Co-founder of Anaconda. Peter leads Anaconda’s AI Incubator, which focuses on advancing core Python technologies and developing new frontiers in open-source AI and machine learning, especially in the areas of edge computing, data privacy, and decentralized computing. George Mathew joins Insight Partners as a Managing Director focused on venture stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market Fit. He brings 20+ years of experience developing high-growth technology startups including most recently being CEO of Kespry. RSVPs now closed Trusted by 90% of Fortune 500 Companies --- > Visit Anaconda’s Booth #P9 at Microsoft Ignite 2024 - Published: 2025-04-02 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/events/microsoft-ignite Anaconda at Microsoft Ignite 2024 Visit Anaconda’s Booth #P9Anaconda and Microsoft are teaming up to showcase Python in Excel. Book a meeting with our experts at the booth, find more information about our products and solutions, and access additional resources below. Don’t forget to check out our MS Ignite Digital Showcase Page for special offers and additional content! Contact Us Meet Anaconda’s Co-Founder, Peter Wang! Tuesday, November 19 | 6:00 PM – 7:00 PM | Anaconda Booth #P9 Network with experts from Anaconda and Microsoft, and take home cool swag! Join Steve Croce, VP of Product, for his demo on “Amplifying your Productivity with Python-Powered Workflows” Thursday, November 21 | 10:30 AM – 10:45 AM | Partner Theater (Exhibit Hall) Click here for session details Following the demo, be sure to attend Microsoft’s breakout session on “Copilot in Excel: Transforming Data Analysis,” Thursday, at 3:45 PM in BRK282. Anaconda Product Resources Learn more about our products below Announcing General Availability of Toolbox in Excel Read More Free Webinar: Introduction to Python in Excel Register here today! 25% off Anaconda Certification: Data Analysis with Python in Excel Use Code: IGNITE24 Anaconda Booth Schedule Tuesday, November 19, 2024 10:30 AM – 5:45 PM Hub (Exhibit Hall) Hours 5:45 PM – 7:15 PM Microsoft and NVIDIA Mixer in the Hub Come by Booth #P9 and Meet Anaconda’s Co-Founder, Peter Wang Wednesday, November 20, 2024 8:30 AM – 6:00 PM Hub (Exhibit Hall) Hours Thursday, November 21, 2024 8:30 AM – 6:00 PM Hub... --- --- ## Leadership - Published: 2025-08-25 - Modified: 2025-08-29 - URL: https://stage.anaconda.com/leadership/test-leadership-bio Laura Sellers Co-President and Chief Product and Technology Officer Laura Sellers, Co-President and Chief Product and Technology Officer at Anaconda where she leads the company’s product strategy and technological innovation. With over 25 years of experience in the technology industry, Laura has established herself as a visionary leader with exceptional expertise in scaling product and engineering teams. Throughout her career, Laura has demonstrated a remarkable ability to align technological capabilities with market needs, driving product strategies that deliver significant value to customers while accelerating company growth. Her deep understanding of both technical and business domains enables her to bridge the gap between innovation and practical application. Prior to joining Anaconda, Laura held key leadership positions at Alteryx and Collibra, where she played instrumental roles in product development and technological advancement. Her experience spans across data analytics, business intelligence, and data management platforms, giving her unique insights into the evolving data ecosystem. Laura holds a Bachelor of Science degree from Iowa State University. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- - Published: 2025-04-18 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/about-us/leadership/laura-sellers Laura Sellers Co-President and Chief Product and Technology Officer Laura Sellers, Co-President and Chief Product and Technology Officer at Anaconda where she leads the company’s product strategy and technological innovation. With over 25 years of experience in the technology industry, Laura has established herself as a visionary leader with exceptional expertise in scaling product and engineering teams. Throughout her career, Laura has demonstrated a remarkable ability to align technological capabilities with market needs, driving product strategies that deliver significant value to customers while accelerating company growth. Her deep understanding of both technical and business domains enables her to bridge the gap between innovation and practical application. Prior to joining Anaconda, Laura held key leadership positions at Alteryx and Collibra, where she played instrumental roles in product development and technological advancement. Her experience spans across data analytics, business intelligence, and data management platforms, giving her unique insights into the evolving data ecosystem. Laura holds a Bachelor of Science degree from Iowa State University. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- - Published: 2025-03-20 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/about-us/leadership/jane-kim Jane kim Co-President and Chief Commercial Officer Jane Kim, Co-President and Chief Commercial Officer at Anaconda, is a seasoned executive with a proven track record in driving revenue growth and scaling high-performing GTM organizations. She leads GTM strategy and execution through the Sales, Customer Success, and Partnerships teams. Jane has over 20 years of experience in sales, technology, and finance. Prior to Anaconda, she was Chief Revenue Officer of CircleCI, a DevOps platform, where she led all customer-facing teams through massive growth and acceleration. Prior experience includes senior revenue leadership roles at Optimizely, SAP, and SuccessFactors, driving revenue growth and high performance team success. Jane also held roles at Thoma Bravo, a technology investment firm, and Goldman Sachs. Jane holds an MBA from Stanford Graduate School of Business and a BA from Columbia University. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- - Published: 2025-03-20 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/about-us/leadership/mark-mitchell Mark Mitchell Senior Vice President, Strategy and Operations Mark Mitchell, Senior Vice President, Strategy & Operations at Anaconda, where he is responsible for setting operational direction and ensuring alignment with long-term strategic goals. He has 20 years of experience leading cross-functional teams to success in banking, consulting, government, and fintech. Mark has led strategy engagements at Accenture Strategy to uncover product insights, create strategic plans, and prepare companies for IPO. He has also held positions in VC and growth equity PE, investing in software and technology companies ranging in value from $100M to $15B. Mark also has 11 years of military experience and was a part of the U. S. Army Special Forces (Green Berets). His post-military career started in investment banking with roles at Goldman Sachs, PIMCO, and RBC Capital Markets, where he advised and structured the sale of over $5. 9B in securities. Mark holds an MBA from Cornell University, an MPA from the University of Pennsylvania, and a Bachelor’s in Economics from the University of California, Riverside. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- - Published: 2025-03-20 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/leadership/megan-niedermeyer Megan Niedermeyer Chief Legal Officer Megan Niedermeyer, Chief Legal Officer at Anaconda, is a seasoned legal and operational leader for high-growth companies. She joined in December 2024 and leads the company’s global legal function and strategy. Prior to Anaconda, Megan was the Chief Legal Officer and Corporate Secretary of Apollo. io, an AI-enabled sales operations platform, leading the company through its Series D fundraise and multiple high-profile litigation events. In her previous role as General Counsel & Corporate Secretary of Fivetran, a data automation platform, Megan led the team through the company’s series D fundraise and acquisition of HVR. As Head of Legal & Compliance at Gusto, a payroll software company, she led the company through its series D fundraise and product pivots to support small businesses in the height of COVID. Prior to going in-house, Megan worked with companies at all stages of the corporate lifecycle while at Cooley LLP. Megan holds a JD from the University of California, Berkeley School of Law, a MSc from the London School of Economics and Political Science, and a BA from the University of California, San Diego. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that... --- - Published: 2025-03-20 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/about-us/leadership/nitin-mittal Nitin Mittal Chief Financial Officer Nitin Mittal, Chief Financial Officer at Anaconda, has more than 20 years of finance experience with over a decade in the software/SaaS and eCommerce industries. He has served in a CFO capacity for two dozen technology companies, leading finance departments at multiple VC and PE-backed businesses with revenues ranging from $5 to $500+ million, employees ranging from 50 to 9,000, and global operations spanning the US, Europe, and Asia. Nitin has structured and negotiated complex equity and debt financing exceeding $360 mm as well as M&A transactions of ~$500 mm, leading the post-merger integrations on over 20 acquisitions. Nitin holds a BS in Electrical Engineering from the University of Michigan and an MS in Biomedical Engineering from Columbia University. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- - Published: 2025-03-20 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/about-us/leadership/peter-wang Peter Wang Chief AI and Innovation Officer and Co-founder Peter Wang is the Chief AI and Innovation Officer and Co-founder of Anaconda. Peter leads Anaconda’s AI Incubator, which focuses on advancing core Python technologies and developing new frontiers in open-source AI and machine learning, especially in the areas of edge computing, data privacy, and decentralized computing. Prior to founding Anaconda, Peter spent 15 years in software design and development across a broad range of areas, including 3D graphics, geophysics, large data simulation and visualization, financial risk modeling, and medical imaging. As a creator of the PyData community and conferences, he devotes time and energy to growing the Python data science community and advocating for increasing data literacy around the world. Peter holds a BA in Physics from Cornell University. Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- - Published: 2025-03-20 - Modified: 2025-08-05 - URL: https://stage.anaconda.com/leadership/vanessa-macllwaine Vanessa Macllwaine Chief People Officer Vanessa MacIlwaine, Chief People Officer at Anaconda, where she leads the company’s global People strategy, focusing on talent acquisition, employee engagement, and organizational development. With over 20 years of experience in human resources across the tech sector, Vanessa has demonstrated expertise in building and leading international teams, developing People strategies, and fostering inclusive workplace cultures. Prior to joining Anaconda, Vanessa served as the Senior Vice President of People at Contentful, where she oversaw the company’s global People function and contributed to the company’s rapid growth and expansion. Her previous experience includes executive leadership roles at Fastly and LendingClub, where she played pivotal roles in scaling those businesses during periods of significant growth. Vanessa holds a BA in Psychology from the University of Washington and has earned certification as a Professional in Human Resources (PHRca). Learn More About Anaconda News Check out Anaconda Newsroom for top press stories, press releases, analyst reports, company updates, and more. See All News Events Join us at an event near you or online. Meet the Anaconda team and find products and services to solve your AI and data science challenges. See All Events Careers Work at Anaconda impacts critical research and future technologies that shape our world. See All Positions. See Open Positions --- --- ## Legal Pages > These terms govern your purchased subscription if noted in your Order Form or relevant purchase document. - Published: 2025-07-17 - Modified: 2025-08-01 - URL: https://stage.anaconda.com/legal/master-subscription-agreement This Master Subscription Agreement ("Agreement") is entered into as of the date of the last signature below ("Effective Date") by and between Anaconda, Inc. , with its principal place of business at 1108 Lavaca Street Suite 110-645, Austin, TX 78701 ("Anaconda"), and the entity identified in the signature block below ("Customer"). This Agreement governs Customer's use of the Anaconda Platform (as defined below). DEFINITIONS“Affiliate” means any corporation or other entity that directly or indirectly controls, is controlled by, or is under common control with the relevant party, where “control” means to have: (i) ownership of more than 50% of the entity in question; or (ii) the power to direct the affairs of the entity through any lawful means. “Anaconda Content” means software, code, metadata, tools, libraries, scripts, APIs, software development kits, templates, algorithms, workflows, user interfaces, links, usage materials, technical specifications, and Documentation. “Anonymized Data” means Personal Data that has been irreversibly anonymized or de-identified so that the Data Subject to whom it originally related cannot be identified in accordance with Data Protection Laws. Anonymized Data will not be considered Personal Data, Customer Content, or Customer Confidential Information. “Anaconda Platform” or “Platform” means, collectively, all of the Offerings, Anaconda Content, and Support Services, including provision of maintenance and Updates, that are provided by or on behalf of Anaconda. “Customer Content” means Packages, software, code, tools, libraries, scripts, APIs, software development kits, templates, algorithms, workflows, user interfaces, links, data, files, attachments, text, images, reports, and any other data that is uploaded,... --- > The Terms of Service for Anaconda’s different websites, Offerings, and separate products or services provided by Anaconda. - Published: 2025-07-15 - Modified: 2025-07-30 - URL: https://stage.anaconda.com/legal/terms/terms-of-service - Categories: News The plain-language summaries provided in italics throughout these Terms of Service are for readability only. They are not legally binding, are not legal advice, and do not replace the full legal terms. In case of any conflict between the summaries and the actual terms, the full legal terms will control. These Terms of Service (“Terms”) are between you and Anaconda, Inc. and its Affiliates (“Anaconda”). By accessing or using the Anaconda Platform or Offerings , you agree to these Terms on behalf of yourself or, if applicable, your employer or another entity. If you accept these Terms using a work or organizational email address, or otherwise on behalf of an entity, you represent and warrant that you have the authority to bind that entity to these Terms. In that case, “you” or “your” means that entity and its Users. By clicking on the “Agree” (or similar button or checkbox) that is presented to you at the time of placing an Order, or by using, downloading, installing, or accessing the Platform or any Offerings (collectively, “using”) , you confirm you are bound by these Terms. If you do not wish to be bound by these Terms, do not click “Agree” (or similar button or checkbox), use, or access the Platform or any Offerings. 1. When You Can Use The Platform For Free When you need a paid license, and when you do not. a. When Your Use is Free. You can use the Platform for free if: (1) you are an... --- > This page lists the legal terms applicable to Anaconda.org and community-provided channels at Anaconda.org. - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/anaconda-org - Categories: News The plain-language summaries provided in italics throughout this Offering Description are for readability only. They are not legally binding and do not replace the full legal terms. In case of any conflict between the summaries and the actual terms, the full legal text will control. These summaries are not legal advice. This Offering Description describes Anaconda. org (hereinafter “Anaconda. org”). Your use of Anaconda. org is governed by this Offering Description, and the Anaconda Terms of Service (the “Terms”, available at www. anaconda. com/legal/terms/terms-of-service), collectively the “Agreement” between you (“You”) and Anaconda, Inc. (“We” or “Anaconda”). In the event of a conflict, the order of precedence is as follows: 1) this Offering Description and 2) the Terms. Capitalized terms used in this Offering Description and/or the Order not otherwise defined herein, have the meaning given to them in the Terms. Anaconda may, at any time, terminate this Agreement and the license granted hereunder if you fail to comply with any term of this Agreement. Anaconda reserves all rights not expressly granted to you in this Agreement. These Anaconda. org terms are between you and Anaconda, Inc. (“Anaconda”). By accessing or using Anaconda. org, you agree to this Offering Description on behalf of yourself or, if applicable, your employer or another entity. If you accept this Offering Description using a work or organizational email address, or otherwise on behalf of a company or other legal entity, you represent and warrant that you have the authority to bind that entity to this... --- > These terms govern Anaconda Toolbox in Microsoft Excel by Anaconda. - Published: 2025-07-15 - Modified: 2025-07-17 - URL: https://10.2.107.56:8443/legal/terms/toolbox - Categories: News This Offering Description describes Anaconda Toolbox in Microsoft Excel (hereinafter the “Toolbox”). Your use of the Toolbox is governed by this Offering Description, and the Anaconda Terms of Service (the “Terms”, available at www. anaconda. com/legal/terms/terms-of-service), collectively the “Agreement” between you (“You”) and Anaconda, Inc. (“We” or “Anaconda”). In the event of a conflict, the order of precedence is as follows: 1) this Offering Description; 2) if applicable, a Custom Agreement; and 3) the Terms if no Custom Agreement is in place. Capitalized terms used in this Offering Description and/or the Order not otherwise defined herein, including in Section 13 (Definitions), have the meaning given to them in the Terms or Custom Agreement, as applicable. Anaconda may, at any time, terminate this Agreement and the license granted hereunder if you fail to comply with any term of this Agreement. Anaconda reserves all rights not expressly granted to you in this Agreement. Anaconda Toolbox Description The Toolbox allows Users to utilize various Anaconda-developed tools, including Anaconda Assistant, within the Microsoft Excel application. These terms complement the existing Anaconda Terms of Service, Anaconda Acceptable Use Policy, and Microsoft Terms and Conditions regarding Python in Excel. While utilizing the Toolbox, Users are subject to both Anaconda’s outlined terms and Microsoft’s broader conditions for Python integration in Excel. Anaconda Assistant Anaconda Assistant is a component included to facilitate Python in Excel coding functionality. The rules concerning data privacy and intellectual property within the context of Anaconda Assistant are governed by the terms specified in... --- > Miniconda End User License Agreement - Published: 2025-07-15 - Modified: 2025-07-16 - URL: https://10.2.107.56:8443/legal/terms/miniconda - Categories: News Copyright Notice: Miniconda® © 2015, Anaconda, Inc.   All rights reserved. Miniconda® is licensed, not sold.   Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer; 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution;  3. The name Anaconda, Inc. or Miniconda® may not be used to endorse or promote products derived from this software without specific prior written permission from Anaconda, Inc. ; and  4. Miniconda® may not be used to access or allow third parties to access Anaconda package repositories if such use would circumvent paid licensing requirements or is otherwise restricted by the Anaconda Terms of Service. DISCLAIMER: THIS SOFTWARE IS PROVIDED BY ANACONDA “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE , AND NON-INFRINGEMENT ARE DISCLAIMED. IN NO EVENT SHALL ANACONDA BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE... --- > This is the Acceptable Use Policy applicable to the Anaconda Platform. - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/acceptable-use - Categories: News At Anaconda, Inc. (“Anaconda”), our mission is to empower individuals with data literacy, enabling them to ask better questions and make informed decisions. To maintain a productive and seamless experience across our Offerings, including all content provided, published, distributed, or transmitted by Anaconda through our Offerings, we require responsible use from our Users. This Acceptable Use Policy (“Policy”) is an integral part of our Terms of Service (“TOS”) and outlines the behaviors and activities that are considered misuse or abuse of our sites and services. By using Anaconda’s Offerings, you agree to adhere to this Policy. Use good judgment, act responsibly, and treat others with respect so that together, we can foster a positive and innovative community. Definitions. A capitalized definition that is not defined in this Section shall have the meaning attributed to it in Section 1 (Definitions) the Anaconda Terms of Service. Enforcement. Anaconda reserves the right to remove content or restrict access to our services if we determine that any activity violates the spirit or intent of this Policy, even if it is not explicitly prohibited. In other words, if conduct appears to be abusive, harmful, or disruptive—regardless of whether it is specifically listed here—we may take appropriate action. Updates. This Policy may be updated periodically. Updates will become effective as specified in the revised Policy or 30 days after posting, whichever is later. Continued use of Anaconda’s services after an update constitutes acceptance of the revised terms. What is Prohibited By This Policy. Disruptive Activities. Disruptive... --- > Anaconda Digital Millennium Copyright Act Policy (DMCA Policy) - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/dmca - Categories: News Pursuant to the Digital Millennium Copyright Act (“DMCA”) (17 U. S. C. § 512), Anaconda, Inc. (“Anaconda”) has implemented procedures for responding to clear written notification of claimed copyright infringements as set forth herein. This page describes the information that should be present in these notices. It is designed to make submitting notices of alleged infringement to Anaconda as straightforward as possible while reducing the number of notices that we receive that are fraudulent or difficult to understand or verify. The form of notice specified below is consistent with the form suggested by the DMCA (the text of which can be found at the U. S. Copyright Office website, http://www. copyright. gov), but Anaconda will respond to notices of this form from other jurisdictions as well. Regardless of whether Anaconda may be liable for such infringement under local country law or United States law, Anaconda’s response to these notices may include removing or disabling access to material claimed to be the subject of infringing activity and/or terminating subscribers. If Anaconda removes or disables access in response to such a notice, Anaconda will make a good faith attempt to contact the owner or administrator of the affected site or content so that they may make a counter notification. Anaconda may also document notices of alleged infringement on which we act. A. Infringement Notification For Allegedly Infringing Materials To file a notice of infringement with Anaconda, you must provide a written communication (by regular mail — not by email, except by prior... --- > These Guidelines are designed to ensure proper legal use of the Anaconda Marks and to prevent confusion that can result from improper or illegal usage. - Published: 2025-07-15 - Modified: 2025-07-12 - URL: https://10.2.107.56:8443/legal/terms/trademark - Categories: News The Anaconda, Inc. (“Anaconda”) trademarks, service marks, logos and designs, as well as other works of authorship that are eligible for copyright protection (collectively, “Marks”), and the goodwill they represent, are among Anaconda’s most valuable assets. To safeguard them, Anaconda has posted these Trademark and Brand Guidelines (“Guidelines”) to assist you in properly using our Marks in the specific cases that we permit. The strength of our Marks depends, in part, upon consistent and appropriate use. We ask that you properly follow these Guidelines and use and credit our Marks strictly in accordance with these Guidelines. We reserve the right to change these Guidelines at any time and solely at our discretion. 1. Guidelines Overview The requirements set forth in these Guidelines are general. Authorized partners and licensees may be subject to additional restrictions that are not set forth in these Guidelines. If you are a partner or a licensee of Anaconda, please consult your agreement for any additional requirements applicable to your use of our Marks. If you are a Licensee but have not been provided with special guidelines for usage of Anaconda Marks, then these Guidelines apply to your usage of Anaconda Marks. In the event of a conflict between the applicable agreement and these Guidelines, the terms of the agreement will control. These Guidelines are designed to ensure proper legal use of the Anaconda Marks and to prevent confusion that can result from improper or illegal usage. These Guidelines should be followed along with Anaconda’s Terms of... --- > Events Code of Conduct for all who participate at Anaconda events - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/events - Categories: News Anaconda, Inc. (“Anaconda”) is committed to providing safe and welcoming environments for all who participate in Anaconda Events. Anaconda prohibits and will not tolerate any form of harassment, bullying, or discrimination. Together, we can ensure that Anaconda Events support free expression and exchange of scientific ideas in environments that are positive and productive for all. Purpose Anaconda has established this Event Code of Conduct (the “Code”) to serve as a guideline for the professional conduct of anyone attending or participating in an Anaconda Event, as well as the consequences for unacceptable behavior. We expect you to follow this Code so that you and other Participants can enjoy the Event responsibly and with respect for the rights of others. Failure to abide by this Code is subject to corrective action and sanctions, including refused admission, ejection, banishment, and other penalties consistent with this Code. Scope And Applicability The Code applies to all attendees (including Anaconda personnel), media representatives, speakers, exhibitors, sponsors, staff, contractors, volunteers, organizers, and other guests (collectively referred to as “Participants”) of official Anaconda programs, conferences, events, meetings, social gatherings, and other activities held, sponsored, or affiliated with Anaconda (each an “Event”). By attending any Anaconda Event, you agree to abide by this Code. Expected Behavior Treat each other well. Every Participant has a right to enjoy their experience without fear of harassment or discrimination. When attending any Anaconda Event: Treat everyone with respect; Be considerate and collaborative; Act in a fair and responsible way; Refrain from demeaning, discriminatory... --- > These Terms of Use govern use of Anconda’s websites. - Published: 2025-07-15 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/website-terms-of-use - Categories: News Anaconda, Inc. or its affiliated companies and subsidiaries (collectively, "Anaconda") operate this website ("Website"). This Website may provide you with access to a variety of web pages, documents, software, services, images, graphics, audio, video, forums, discussion groups, blogs, and other content ("Content"). By accessing or logging into the Website, you agree to these website terms of use (the “Terms”). Anaconda may revise these terms at any time. Your continued use of the Website after any such revisions have been published constitutes your agreement to the revised terms. If you do not agree to these terms, do not use the Website. Personal data collected when you access and use the Website will be processed by Anaconda in accordance with the Anaconda Privacy Notice. For more information on the types of personal data collected on the Website, how Anaconda handles your personal data and other related topics, please see the Anaconda Privacy Notice, which is incorporated here by reference. For the avoidance of doubt, these Terms do not control or govern your use of Anaconda products or services, Please visit the anaconda. com/legal page to see the applicable agreement(s) related to your use of Anaconda products and services.   1. Anaconda and Third Party Content. When using the Website, you may be able to access or download certain Content provided by Anaconda ("Anaconda Content"), or Content provided by third parties ("Third Party Content"). Content is governed by the license that accompanies it. By using or downloading any Content, you agree to the... --- > This Academic Policy (this “Policy”) outlines eligibility criteria, benefits, discounting eligibility, and how to sign up for Anaconda’s Academic Program. - Published: 2025-07-14 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/academic As part of Anaconda’s deep and ongoing commitment to our communities, Anaconda is proudly committed to supporting Eligible Non-Profit and Research Organizations in their mission by providing discounted access to the Anaconda Platform. This Academic Policy (this “Policy”) outlines eligibility criteria, benefits, discounting eligibility, and how to sign up for Anaconda’s Academic Program. Anaconda, Inc. (“Anaconda“) recognizes the vital role academic institutions play in developing the next generation of open-source developers and code-environment thought leaders. To support this mission, Anaconda is proud to provide individuals who are using Anaconda’s Platform and Offerings on behalf of or in association with an Eligible Academic Institution with free access to the Anaconda Platform through registration and participation in the Anaconda for Education Program (the “Program”). This Academic Policy (this “Policy“) sets forth the eligibility criteria, benefits, application process, and terms governing participation in the Program. 1. Definitions Capitalized terms used but not defined in this Policy have the meanings given to them in Anaconda’s Terms of Service. 2. Eligibility Criteria 2. 1 Eligible Academic InstitutionsTo qualify for the Program, the organization you are using the Anaconda Platform or Offerings on behalf of must meet the following criteria to be designated as an Eligible Academic Institution: An “Eligible Academic Institution” means an organization that is accredited as a college, university, or other higher education institution by a recognized national, regional, or local agency, including but not limited to: The U. S. Department of Education (or any successor agency); The Council for Higher Education Accreditation;... --- > This page describes the terms applicable to use of Anaconda’s Platform and Offerings via our Partner’s solutions. - Published: 2025-07-14 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/embedded By downloading, installing, or using the Anaconda Platform or any Offering through a Partner Solution, you ("End Customer") agree to be bound by these Embedded End Customer Terms (the "Supplemental Terms"), which amend and are incorporated into the Anaconda Terms of Service ("Terms"), solely with regard to the Anaconda Platform and Offerings hosted and provided by Anaconda, Inc. ("Anaconda") through a Partner Solution. These Supplemental Terms, together with the Terms, as applicable, govern all integrations to and from the Anaconda Platform and Offerings. In the event of any inconsistency between the Terms and these Supplemental Terms, these Supplemental Terms shall prevail with respect to your use of the Anaconda Platform and Offerings through a Partner Solution. By downloading, installing, or using the Anaconda Platform or any Offering through a Partner Solution, End Customer represents and affirms that End Customer has read, understands, and agrees to be legally bound by and comply with these Supplemental Terms. If End Customer does not agree with these Supplemental Terms, End Customer is not authorized to use the Anaconda Platform or any Offering in any manner through a Partner Solution. NOTWITHSTANDING THE FOREGOING, IF END CUSTOMER HAS NEGOTIATED A SEPARATE COMMERCIAL AGREEMENT WITH ANACONDA, WHICH GOVERNS END CUSTOMER'S USE OF THE ANACONDA PLATFORM OR OFFERINGS, THE TERMS AND CONDITIONS OF THESE SUPPLEMENTAL TERMS SHALL SUPERSEDE THE TERMS AND CONDITIONS OF SUCH NEGOTIATED AGREEMENT TO THE EXTENT THEY ARE INCONSISTENT. Capitalized terms used in these Supplemental Terms and not otherwise defined herein are defined in the... --- > This page describes Anaconda’s Non-Profit and Research Policy, including eligibility requirements. - Published: 2025-07-14 - Modified: 2025-08-06 - URL: https://stage.anaconda.com/legal/non-profit-research As part of Anaconda’s deep and ongoing commitment to our communities, Anaconda, Inc. (“Anaconda“) is proudly committed to supporting Eligible Non-Profit and Research Organizations in their mission by providing discounted access to the Anaconda Platform through its Non-Profit and Research Program (the “Program“). This Non-Profit and Research Policy (the "Policy") outlines eligibility criteria, benefits, application process, and terms governing participation in the Program. 1. Definitions Capitalized terms used but not defined in this Policy shall have the meanings given to them in Anaconda's Terms of Service 2. Eligibility Criteria 2. 1 Eligible Non-Profit and Research Organizations To qualify for Anaconda's Non-Profit and Research Program, the organization you are using the Anaconda Platform or Offerings on behalf of must meet one of the following criteria to be considered an Eligible Non-Profit or Research Organization: An "Eligible Non-Profit or Research Organization" means: A 501(c)(3) or equivalent nonprofit organization in good standing and with annual revenue under $1 billion USD; An internationally recognized nonprofit or charitable organizations in good standing under their respective national laws (such as registered charities, foundations, associations, and NGOs) with annual revenue under $1 billion USD; A research organization with annual revenue under $1 billion USD, including any entity, group, or institution primarily engaged in conducting, funding, or disseminating research, analysis, or studies; A healthcare organization with annual revenue under $1 billion USD; A non-profit organization that is not an Ineligible or Research Non-Profit Organization (as defined below); or A non-profit organization otherwise deemed by Anaconda to be eligible... --- > This page details the legal terms applicable to Edublocks.org - Published: 2025-07-14 - Modified: 2025-07-15 - URL: https://10.2.107.56:8443/legal/terms/edublocks The plain-language summaries provided in italics throughout these Terms of Service are for readability only. They are not legally binding and do not replace the full legal terms. In case of any conflict between the summaries and the actual terms, the full legal text will control. These summaries are not legal advice. PLEASE READ THESE EDUBLOCKS TERMS OF SERVICE (THE “TERMS”) CAREFULLY BEFORE ACCESSING OR USING (COLLECTIVELY, “USE” OR “USING”) THE EDUBLOCKS WEBSITE OR SERVICES (THE “EDUBLOCKS SERVICES”). BY USING THE EDUBLOCKS SERVICES, YOU SIGNIFY YOUR ASSENT TO AND ACCEPTANCE OF THESE TERMS AND ACKNOWLEDGE YOU HAVE READ AND UNDERSTAND THESE TERMS. IF YOU ARE AN INDIVIDUAL ACTING ON BEHALF OF A SCHOOL OR OTHER ENTITY, YOU REPRESENT THAT YOU HAVE THE AUTHORITY TO ENTER INTO THESE TERMS ON BEHALF OF THAT ENTITY. 1. Terms Applicability and Term When these Terms apply and how long they last. a. Terms Applicability. These Terms govern Your use of the EduBlocks Services. These Terms are between Anaconda, Inc. on behalf of itself and its Affiliates (“EduBlocks”) and: (i) you, if you are an individual using the EduBlocks Services; or (ii) the school or other entity you are acting on behalf of if you are a teacher or other individual acting on behalf of a school or other entity (collectively schools, entities, and teachers and individuals acting on behalf of schools and entities are “Entity Users”). Together, individuals noted in (i) and Entity Users noted (ii) are referred to as “You” in these Terms.... --- ---