خرید و دانلود نسخه کامل کتاب LLM Engineer’s Handbook: Master the art of engineering large language models from concept to production

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practicesPurchase 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 FaceBook DescriptionThis 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 learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles like IaCPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsExplore continuous training, monitoring, and logic automationLearn about RAG ingestion as well as inference and feature pipelinesWho this book is forThis 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 ContentsUndersstanding the LLM Twin Concept and ArchitectureTooling and InstallationData EngineeringRAG Feature PipelineSupervised Fine-tuningFine-tuning with Preference AlignmentEvaluating LLMsInference OptimizationRAG Inference PipelineInference Pipeline DeploymentMLOps and LLMOpsAppendix: MLOps Principles