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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

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

3. Experiment tracking

Training ML models is an entirely iterative and experimental process. Unlike traditional software development, it involves running multiple parallel experiments, comparing them based on a set of predefined metrics, and deciding which one should advance to production. An experiment tracking tool allows you to log all the necessary information, such as metrics and visual representations of your model predictions, to compare all your experiments and easily select the best model. Popular tools are Comet ML, W&B, MLflow, and Neptune.

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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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