Essential Guide to LLMOps
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Essential Guide to LLMOps

Implementing effective strategies for Large Language Models in deployment and continuous improvement

About the Book

The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications. This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You’ll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You’ll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications. By the end of this book, you’ll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI.

About the Author

Packt Publishing Ltd
Packt Publishing Ltd

Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com

Table of Contents

  1. Introduction to LLMs and LLMOps
  2. Reviewing LLMOps Components
  3. Processing Data in LLMOps Tools
  4. Developing Models via LLMOps
  5. LLMOps Review and Compliance
  6. LLMOps Strategies for Inference, Serving, and Scalability
  7. LLMOps Monitoring and Continuous Improvement
  8. The Future of LLMOps and Emerging Technologies

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