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.

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Spis treści

LLM Engineer's Handbook. Master the art of engineering large language models from concept to production eBook -- spis treści

• 1. Understanding the LLM Twin Concept and Architecture
• 2. Tooling and Installation
• 3. Data Engineering
• 4. RAG Feature Pipeline
• 5. Supervised Fine-Tuning
• 6. Fine-Tuning with Preference Alignment
• 7. Evaluating LLMs
• 8. Inference Optimization
• 9. RAG Inference Pipeline
• 10. Inference Pipeline Deployment
• 11. MLOps and LLMOps