LLM Engineer's Handbook

Artificial intelligence is experiencing rapid development, and large language models (LLMs) play a key role in this revolution. This book offers deep insights into the design, training, and deployment of LLMs in real-world scenarios, using best MLOps practices. The book addresses the creation of an efficient, scalable, and modular system based on LLMs, going beyond traditional Jupyter notebooks and focusing on building production solutions.

You will explore the fundamental aspects of data engineering, fine-tuning using supervised learning, and the deployment process. Practical examples, such as creating a LLM Twin, will help you implement key MLOps components into your own projects. The book also covers advanced technologies in output optimization, preference alignment, and real-time data processing, making it an indispensable resource for engineers working with language models.

By the end of the reading, you will have mastered the skills for deploying LLMs capable of solving practical tasks with minimal latency and high availability. This book will be useful for both beginner AI specialists and experienced practitioners looking to deepen their knowledge and skills.

Who is this book for?

The book is intended for AI engineers, natural language processing specialists, and LLM engineers looking to deepen their knowledge of language models. A basic understanding of LLMs, generative AI, Python, and AWS is recommended. Regardless of your level of preparation, you will receive comprehensive guidance on applying LLMs in real-world scenarios.

What you will learn:

• Implement robust data pipelines and manage LLM training cycles
• Create your own LLMs and optimize them through practical examples
• Master the basics of LLMOps through key concepts such as orchestrators and prompt monitoring
• Perform supervised fine-tuning and model evaluation
• Deploy comprehensive LLM-based solutions using AWS and other tools
• Design scalable and modular LLM systems
• Explore the application of Retrieval-Augmented Generation (RAG) by building functions and data output pipelines