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LLM Engineer's Handbook

You're reading from   LLM Engineer's Handbook Master the art of engineering large language models from concept to production

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836200079
Length 522 pages
Edition 1st Edition
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Authors (3):
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Maxime Labonne Maxime Labonne
Author Profile Icon Maxime Labonne
Maxime Labonne
Paul Iusztin Paul Iusztin
Author Profile Icon Paul Iusztin
Paul Iusztin
Alex Vesa Alex Vesa
Author Profile Icon Alex Vesa
Alex Vesa
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Table of Contents (15) Chapters Close

Preface 1. Understanding the LLM Twin Concept and Architecture 2. Tooling and Installation FREE CHAPTER 3. Data Engineering 4. RAG Feature Pipeline 5. Supervised Fine-Tuning 6. Fine-Tuning with Preference Alignment 7. Evaluating LLMs 8. Inference Optimization 9. RAG Inference Pipeline 10. Inference Pipeline Deployment 11. MLOps and LLMOps 12. Other Books You May Enjoy
13. Index
Appendix: MLOps Principles

MLOps and LLMOps tooling

This section will quickly present all the MLOps and LLMOps tools we will use throughout the book and their role in building ML systems using MLOps best practices. At this point in the book, we don’t aim to detail all the MLOps components we will use to implement the LLM Twin use case, such as model registries and orchestrators, but only provide a quick idea of what they are and how to use them. As we develop the LLM Twin project throughout the book, you will see hands-on examples of how we use all these tools. In Chapter 11, we will dive deeply into the theory of MLOps and LLMOps and connect all the dots. As the MLOps and LLMOps fields are highly practical, we will leave the theory of these aspects to the end, as it will be much easier to understand it after you go through the LLM Twin use case implementation.

Also, this section is not dedicated to showing you how to set up each tool. It focuses primarily on what each tool is used for and highlights...

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LLM Engineer's Handbook
Published in: Oct 2024
Publisher: Packt
ISBN-13: 9781836200079
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