[computer-internet] LLM Engineer's Handbook by Maxime Labonne EPUB

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

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

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