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Textbook in PDF format

Key Features:
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
Book 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.
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
Who this book is 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.
Table of Contents:
Introduction to LLMs and LLMOps
Reviewing LLMOps Components
Processing Data in LLMOps Tools
Developing Models via LLMOps
LLMOps Review and Compliance
LLMOps Strategies for Inference, Serving, and Scalability
LLMOps Monitoring and Continuous Improvement
The Future of LLMOps and Emerging Technologies