LLM Engineer's Handbook: Master the art of engineering large language models from concept to production (大型語言模型工程師手冊：從概念到生產，掌握工程的藝術)

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices

Purchase of the print or Kindle book includes a free eBook in PDF format

"This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world." - Julien Chaumond, CTO and Co-founder, Hugging Face

Book Description

This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. The guide walks you through building an LLM-powered twin that's cost-effective, scalable, and modular. It moves beyond isolated Jupyter Notebooks, focusing on how to build production-grade end-to-end LLM systems.

Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.

What you will learn

- Implement robust data pipelines and manage LLM training cycles

- Create your own LLM and refine with the help of hands-on examples

- Get started with LLMOps by diving into core MLOps principles like IaC

- Perform supervised fine-tuning and LLM evaluation

- Deploy end-to-end LLM solutions using AWS and other tools

- Explore continuous training, monitoring, and logic automation

- Learn about RAG ingestion as well as inference and feature pipelines

Who this book is for

This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.

Table of Contents

- Undersstanding the LLM Twin Concept and Architecture

- Tooling and Installation

- Data Engineering

- RAG Feature Pipeline

- Supervised Fine-tuning

- Fine-tuning with Preference Alignment

- Evaluating LLMs

- Inference Optimization

- RAG Inference Pipeline

- Inference Pipeline Deployment

- MLOps and LLMOps

- Appendix: MLOps Principles