LLM Engineer's Handbook

0h 0m 0s
English
Paid
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

About the Authors

Maxime Labonne

Maxime Labonne thumbnail

Maxime Labonne is the head of the post-training models department at Liquid AI. He holds a Ph.D. in machine learning from the Polytechnic Institute of Paris and is a certified Google Developer Expert in AI/ML.

Maxim has made significant contributions to the open-source community by creating educational materials, including the LLM Course, instructional guides on fine-tuning models, and tools such as LLM AutoEval. Under his leadership, advanced models like NeuralDaredevil have been developed.

Maxim is also the author of bestsellers “The LLM Engineer's Handbook” and “Practical Applications of Graph Neural Networks Using Python”, which have become essential reads for machine learning professionals.

Paul Iusztin

Paul Iusztin thumbnail

Pol Justin is a senior machine learning and MLOps engineer at Metaphysic, a leading generative AI platform, where he is one of the key specialists in bringing deep learning products to production. With over seven years of experience, he has worked on developing solutions in generative AI, computer vision, and MLOps for companies such as CoreAI, Everseen, and Continental.

Pol has an unwavering passion for developing high-impact AI/ML products that bring real benefits to the world, as well as a commitment to teaching others about this process. He is the founder of Decoding ML, a training project with practice-proven content, where he shares knowledge on designing, programming, and deploying industrial-level machine learning solutions.

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