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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

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781836200079

Length 522 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Artificial Intelligence

Authors (3):

Maxime Labonne

Paul Iusztin

Alex Vesa

View More author details

Table of Contents (15) Chapters

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

Python ecosystem and project installation

Any Python project needs three fundamental tools: the Python interpreter, dependency management, and a task execution tool. The Python interpreter executes your Python project as expected. All the code within the book is tested with Python 3.11.8. You can download the Python interpreter from here: https://www.python.org/downloads/. We recommend installing the exact Python version (Python 3.11.8) to run the LLM Twin project using pyenv, making the installation process straightforward.

Instead of installing multiple global Python versions, we recommend managing them using pyenv, a Python version management tool that lets you manage multiple Python versions between projects. You can install it using this link: https://github.com/pyenv/pyenv?tab=readme-ov-file#installation.

After you have installed pyenv, you can install the latest version of Python 3.11, using pyenv, as follows:

pyenv install 3.11.8

Now list all installed Python...

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Authors (2)

Paul Iusztin

Paul Iusztin

Paul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. Paul's determined passion and mission are to build data-intensive AI/ML products that serve the world and educate others about the process. As the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade ML, Paul has significantly enriched the engineering and MLOps community. His weekly content on ML engineering and his open-source courses focusing on end-to-end ML life cycles, such as Hands-on LLMs and LLM Twin, testify to his valuable contributions.

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Maxime Labonne

Maxime Labonne

Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.

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