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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

The path to LLMOps: Understanding its roots in DevOps and MLOps

To understand LLMOps, we have to start with the field’s beginning, which is DevOps, as it inherits most of its fundamental principles from there. Then, we will move to MLOps to understand how the DevOps domain was adapted to support ML systems. Finally, we will explain what LLMOps is and how it emerged from MLOps after the widespread adoption of LLMs.

DevOps

Manually shipping software is time-consuming, error-prone, involves security risks, and doesn’t scale. Thus, DevOps was born to automate the process of shipping software at scale. More specifically, DevOps is used in software development, where you want to completely automate your building, testing, deploying, and monitoring components. It is a methodology designed to shorten the development lifecycle and ensure continuous delivery of high-quality software. It encourages collaboration, automates processes, integrates workflows, and implements...

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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.

See other products by Paul Iusztin

Maxime Labonne

Maxime Labonne

Head of Post-Training, Liquid AI

See other products by Maxime Labonne

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