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.

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