As organizations move from experimentation to production-grade GenAI systems, traditional MLOps alone isn’t enough. Below, we share a direct excerpt from >Generative AI in Action by Amit Bahree (Manning, 2024), covering key practices for LLMOps, monitoring, and deployment checklists.
The following text is excerpted with permission.
LLMOps and MLOps
Machine learning operations (MLOps) apply DevOps principles and best practices to develop, deploy, and manage ML models and applications. MLOps aims to streamline the ML lifecycle, from data preparation and experimentation to model training and serving, while ensuring quality, reliability, and scalability.
LLMOps is a specialized domain within MLOps that focuses on the operational aspects of LLMs. LLMs are deep learning models that can generate natural language text and perform various natural language processing (NLP) tasks based on the input provided. Examples of LLMs include GPT-4, BERT, and similar advanced AI systems.
LLMOps introduces tools and best practices that help manage the lifecycle of LLMs and LLM-powered applications, such as prompt engineering, fine-tuning, deployment, monitoring, and governance. LLMOps also addresses the unique challenges and risks associated with LLMs, such as bias, hallucination, prompt injection, and ethical concerns.
Both LLMOps and MLOps share some common goals and challenges, such as automating and orchestrating the ML pipeline; ensuring reproducibility, traceability, and versioning of data, code, models, and experiments; monitoring and optimizing the performance, availability, and resource utilization of models and applications in production; implementing security, privacy, and compliance measures to protect data and models from unauthorized access and misuse; and incorporating feedback loops and continuous improvement cycles to update and refine models and applications based on changing requirements and user behavior.
However, LLMOps and MLOps also have some distinct differences, and switching from MLOps to LLMOps is a paradigm shift—specifically in data, model complexity (including size), and model output in the context of generation:
- Data—LLMs are pretrained on massive text datasets, such as the Common Crawl corpus, and can be adapted for specific use cases using prompt engineering and fine-tuning techniques. This reduces the need for extensive data collection and labeling and introduces the risk of data leakage and contamination from the pretraining data.
- Computational resources—GenAI models, such as LLMs, are very large and complex, often consisting of billions of parameters and requiring specialized hardware and infrastructure to train and run, such as high-end GPUs, memory, and so forth. This poses significant challenges for model storage, distribution, inference, cost, and energy efficiency. This challenge is further amplified when we want to scale up to many users to handle incoming requests without compromising performance.
- Model generation—LLMs are designed to generate coherent and contextually appropriate text rather than adhering to factual accuracy. This leads to various risks, such as bias amplification, hallucination, prompt injection, and ethical concerns. These risks require careful evaluation and mitigation strategies, such as responsible AI frameworks, human oversight, and explainability tools.
Table 1 outlines key differences in the shift to LLMOps from MLOps.
Table 1: Key differences in the shift to LLMOps from MLOps
| Area | Traditional MLOps | LLMOps |
|---|---|---|
| Target audience | ML engineers, data scientists | Application developers, ML engineering, and data scientists |
| Components | Model, data, inference environments, features | LLMs, prompts, tokens, generations, APIs, embeddings, vector databases |
| Metrics | Accuracy (F1 score, precision, recall, etc.) | Quality (similarity), groundedness (accuracy), cost (tokens), latency, evaluations (Perplexity, BLEU, ROUGE, etc.) |
| Models | Typically built from scratch | Typically, prebuilt with inference via an API and multiple versions in production simultaneously |
| Ethical concerns | Bias in training data | Misuse and generation of harmful, fake, and biased output |
Why LLMOps and MLOps?
LLMOps and MLOps are key to the responsible and efficient deployment of LLMs and ML models, ensuring ethical and performance standards. They address problems such as slow development, inconsistent model quality, and high costs, while providing advantages such as speed, consistency, and risk management. LLMOps covers tools and practices for managing LLMs, including prompt engineering, fine-tuning, and governance, resulting in faster development, better quality, cost reduction, and risk control.
Given their complexity, effective management is critical for generative AI models’ performance and cost efficiency. Important factors in LLMOps include model selection, deployment strategies, and version control. The right model size and configuration are essential, possibly customized to specific data. Options between cloud services and private infrastructure balance convenience and data security. Versioning and automated pipelines support smooth updates and rollbacks, enabling continuous integration and deployment. Adopting LLMOps ensures the successful, ethical use of generative AI, maximizing benefits and minimizing risks.
LLMOps and MLOps are crucial for the production deployment of AI applications. They provide the necessary infrastructure to ensure that AI applications are operational, sustainable, responsible, and capable of scaling according to user demand. For developers and technical professionals, these frameworks offer a way to maintain quality assurance, follow compliance and ethical standards, and cost-effectively manage AI applications. In an enterprise environment where reliability and scalability are vital, LLMOps and MLOps are essential for successfully integrating AI technology.
Monitoring and Telemetry Systems
While capable of delivering high-value business outcomes, powerful LLMs require careful monitoring and management to ensure optimal performance, accuracy, security, and user experience. Monitoring is an important part of LLMOps and MLOps, as it shows how well models and applications work in production.
Continuous monitoring is vital for LLMOps, as for many production systems. It helps LLMOps teams solve problems quickly, ensuring the system is speedy and dependable. Monitoring covers performance metrics, such as response time, throughput, and resource utilization, enabling quick intervention if there are delays or performance declines. Telemetry tracking is crucial in this process, providing valuable insights into the model’s behavior and enabling continuous improvement.
Moreover, ethical AI deployment must check for bias or harmful outputs. Using fairness-aware monitoring methods, LLMOps teams ensure that LLMs work ethically, minimizing unwanted biases and increasing user trust. Frequent model updates and maintenance, supported by automated pipelines, ensure that the LLM stays current with the latest developments and data trends, ensuring continued effectiveness and adaptability.
Checklist for Production Deployment
Let’s summarize some advice into a simple checklist that can be handy as a reference guide when deploying applications to production. Of course, as with most of this advice, this is incomplete and should be used as part of the wider set of responsibilities:
Scaling and Deployment
- Assess computational resources—Determine your generative AI models’ hardware and software requirements and ensure the infrastructure can support them effectively.
- Quality and availability of data—Implement robust data validation, quality control processes, and continuous monitoring to ensure data accuracy and relevance.
- Model performance and reliability—Set up regular testing and validation processes to monitor models’ performance. Plan for redundancy, failover, and disaster recovery to ensure high availability.
- Security and compliance—Apply encryption, access controls, and regular compliance audits. Ensure that your models adhere to regulations such as GDPR or HIPAA.
- Cost management—Closely monitor and manage the costs of deploying and maintaining your models. Be prepared to make tradeoffs between cost and performance.
- System integration—Ensure that the generative AI models can be easily integrated into existing systems and workflows.
- Human in the loop—Design the models to include human oversight and intervention where necessary.
- Ethical considerations—When deploying your models, address ethical implications, such as bias and fairness.
Best Practices for Production Deployment
- Metrics for LLM inference—Focus on key metrics such as time to first token (TTFT), time per output token (TPOT), latency, and throughput. Use tools such as MLflow to track these metrics.
- Manage latency—Understand different latency points, and measure them accurately. Consider the influence of prompt size and model size on latency.
- Scalability—Utilize PTUs and PAYGO models to scale your application effectively. Use API management for queuing, rate throttling, and managing usage quotas.
- Quotas and rate limits—Implement strategies to manage quotas and rate limits effectively, including understanding your limits, monitoring usage, and implementing retry logic.
- Observability—Use tools such as MLflow, Traceloop, and Prompt flow to monitor, log, and trace your application for improved performance and user experience.
- Security and compliance—Encrypt data, control access, conduct compliance audits, and deploy anomaly detection systems.
LLMOps and MLOps
- Adopt LLMOps and MLOps frameworks—Ensure that your application follows best practices in LLMOps and MLOps for maintainable, ethical, and scalable AI solutions.
- Monitoring and telemetry systems—Use fairness-aware monitoring methods and telemetry tracking to ensure ethical AI deployment and continuous improvement of your models.
Learn How the Anaconda AI Platform Can Help
The Anaconda AI Platform brings together LLMOps, secure infrastructure, and advanced observability tooling—so your team can deploy GenAI features with confidence. Contact our team to learn how Anaconda can help you innovate with AI safely and securely.
