LLM Engineer’s Handbook: Master the art of engineering Large Language Models from concept to production

Step into the world of LLMs with a practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Key Features Build and refine LLMs with step-by-step examples covering data preparation, RAG, and fine-tuning Master essential skills for deploying and monitoring LLMs, ensuring optimal performance in production Discover cutting-edge methods to enhance LLM performance and adaptability in real-world applications Book Description The field of Artificial Intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. This comprehensive guide walks you through building an end-to-end LLM-powered technical content writer, by overcoming isolated Jupyter Notebooks and focusing on teaching 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, combined with detailed examples, helps you understand the implementation of MLOps components in your projects. The book also explores the cutting-edge advancements in the field, including inference optimization and real-time data processing, making it a vital resource for anyone looking to leverage LLMs in their projects. By the end of this book, you will be proficient in deploying robust large language models, leveraging them to solve practical problems, and maintaining low-latency and high-availability inference capabilities. Whether you are new to AI or an experienced practitioner, this book offers valuable insights and practical knowledge to enhance your expertise in LLMs. What you will learn Implement robust data pipelines and manage LLM training cycles Construct and refine LLMs with hands-on examples Get up and running with MLOps principles like IaC Perform supervised fine-tuning and evaluate LLMs Deploy end-to-end LLM solutions using AWS and other tools Explore continuous training and updating models in production Learn about RAG ingestion and inference pipeline 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 Introduction/Architecture Data Engineering Raw Data Ingestion Pipeline Supervised Fine-tuning LLM Evaluation Preference Alignment Inference Optimization RAG Ingestion RAG Inference Pipeline Inference Pipeline Deployment LLM: Operations and Observability Case Studies — ASIN: 1836200072 | ISBN13: 9781836200079 | ISBN-13: 978-1836200079