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Essential Guide to LLMOps
Essential Guide to LLMOps

Essential Guide to LLMOps: Implementing effective strategies for Large Language Models in deployment and continuous improvement

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Essential Guide to LLMOps

Part 1: Foundations of LLMOps

In the first section of this book, we review the foundational elements that constitute large language model operations (LLMOps). This part sets the stage for understanding the crucial aspects and underlying mechanics that support the efficient use and management of LLMs across various domains.

This part contains the following chapters:

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Key benefits

  • Gain a comprehensive understanding of LLMOps, from data handling to model governance
  • Leverage tools for efficient LLM lifecycle management, from development to maintenance
  • Discover real-world examples of industry cutting-edge trends in generative AI operation
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.

What you will learn

  • Understand the evolution and impact of LLMs in AI
  • Differentiate between LLMOps and traditional MLOps
  • Utilize LLMOps tools for data analysis, preparation, and fine-tuning
  • Master strategies for model development, deployment, and improvement
  • Implement techniques for model inference, serving, and scalability
  • Integrate human-in-the-loop strategies for refining LLM outputs
  • Grasp the forefront of emerging technologies and practices in LLMOps
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2024
Length: 190 pages
Edition : 1st
Language : English
ISBN-13 : 9781835887509
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Product Details

Publication date : Jul 31, 2024
Length: 190 pages
Edition : 1st
Language : English
ISBN-13 : 9781835887509
Category :
Languages :
Tools :

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Table of Contents

13 Chapters
Part 1: Foundations of LLMOps Chevron down icon Chevron up icon
Chapter 1: Introduction to LLMs and LLMOps Chevron down icon Chevron up icon
Chapter 2: Reviewing LLMOps Components Chevron down icon Chevron up icon
Part 2: Tools and Strategies in LLMOps Chevron down icon Chevron up icon
Chapter 3: Processing Data in LLMOps Tools Chevron down icon Chevron up icon
Chapter 4: Developing Models via LLMOps Chevron down icon Chevron up icon
Chapter 5: LLMOps Review and Compliance Chevron down icon Chevron up icon
Part 3: Advanced LLMOps Applications and Future Outlook Chevron down icon Chevron up icon
Chapter 6: LLMOps Strategies for Inference, Serving, and Scalability Chevron down icon Chevron up icon
Chapter 7: LLMOps Monitoring and Continuous Improvement Chevron down icon Chevron up icon
Chapter 8: The Future of LLMOps and Emerging Technologies Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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