What this book covers
Chapter 1, Introduction to LLMs and LLMOps, compares LLMOps to traditional MLOps, highlighting the need for specialized approaches in AI development, deployment, and management. We will look at current trends in LLM applications across various industries, focusing on real-world uses, opportunities, and the importance of stringent security in LLM deployment. Core aspects of LLMOps, including model architecture, training methodologies, evaluation metrics, and deployment strategies, will be explored. We will also look at LLMOps in different applications and the intricacies of their operation and deployment.
Chapter 2, Reviewing LLMOps Components, discusses data collection, preprocessing, and how to ensure the dataset’s quality and diversity. We will also look at developing and fine-tuning the model to ensure the right fit for the desired use case. This chapter also explores governance and review processes to ensure model accuracy, security, and reliability; inference, serving, and ensuring scalability to handle the demands of large-scale use and varied user interactions; and monitoring and continuous improvement to track performance and respond to user feedback.
Chapter 3, Processing Data in LLMOps Tools, looks at collecting, transforming, preparing, and automating data processes within LLMOps to enhance the efficiency and effectiveness of LLMs.
Chapter 4, Developing Models via LLMOps, covers creating, storing, and retrieving features; selecting foundation models; fine-tuning models; tuning hyperparameters; and automating model development to streamline model creation and deployment.
Chapter 5, LLMOps Review and Compliance, looks at how to evaluate LLM performance metrics offline, secure and govern models with LLMOps, ensure legal and regulatory compliance, and operationalize compliance and performance management.
Chapter 6, LLMOps Strategies for Inference, Serving, and Scalability, looks at inference strategies in LLMOps, optimizing model serving for performance, increasing model reliability, and scaling models cost-effectively.
Chapter 7, LLMOps Monitoring and Continuous Improvement, covers monitoring LLM fundamentals, reviewing monitoring tools and technologies, monitoring for metrics, learning from human feedback, incorporating continuous improvement, and synthesizing these elements into a cohesive strategy.
Chapter 8, The Future of LLMOps and Emerging Technologies, looks at identifying trends in LLM development, exploring emerging technologies in LLMOps, considering responsible AI, and developing talent and skill, as well as planning and risk management in the evolving field of LLMOps.
