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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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Toc

Table of Contents (15) Chapters Close

Preface 1. Understanding the LLM Twin Concept and Architecture FREE CHAPTER 2. Tooling and Installation 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

Who this book is for

This book is intended for a wide range of technology professionals and enthusiasts interested in the practical applications of LLMs. It’s ideal for software engineers aiming to transition into AI projects. While some familiarity with software development is beneficial, the book explains many concepts from the ground up, making it accessible even to those who are new to AI and machine learning.

For those already working with machine learning , this book will enhance your skills in implementing and deploying LLM-based systems. We provide a deep dive into the fundamentals of MLOps, guiding you through the process of creating a minimum viable product using an open-source LLM to solve real-world problems.

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