E-Book, Englisch, 522 Seiten
Iusztin / Sen / Labonne LLM Engineer's Handbook
1. Auflage 2025
ISBN: 978-1-83620-006-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
Master the art of engineering large language models from concept to production
E-Book, Englisch, 522 Seiten
ISBN: 978-1-83620-006-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection
Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers 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.
By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.
Weitere Infos & Material
LLM Engineer’s Handbook
Master the art of engineering large language models from concept to production
Paul Iusztin
Maxime Labonne
LLM Engineer’s Handbook
Copyright © 2024 Packt Publishing
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First published: October 2024
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ISBN 978-1-83620-007-9
Forewords
As my co-founder at Hugging Face, Clement Delangue, and I often say, AI is becoming the default way of building technology.
Over the past 3 years, LLMs have already had a profound impact on technology, and they are bound to have an even greater impact in the coming 5 years. They will be embedded in more and more products and, I believe, at the center of any human activity based on knowledge or creativity.
For instance, coders are already leveraging LLMs and changing the way they work, focusing on higher-order thinking and tasks while collaborating with machines. Studio musicians rely on AI-powered tools to explore the musical creativity space faster. Lawyers are increasing their impact through retrieval-augmented generation (RAG) and large databases of case law.
At Hugging Face, we’ve always advocated for a future where not just one company or a small number of scientists control the AI models used by the rest of the population, but instead for a future where as many people as possible—from as many different backgrounds as possible—are capable of diving into how cutting-edge machine learning models actually work.
Maxime Labonne and Paul Iusztin have been instrumental in this movement to democratize LLMs by writing this book and making sure that as many people as possible can not only use them but also adapt them, fine-tune them, quantize them, and make them efficient enough to actually deploy in the real world.
Their work is essential, and I’m glad they are making this resource available to the community. This expands the convex hull of human knowledge.
As someone deeply immersed in the world of machine learning operations, I’m thrilled to endorse . This comprehensive guide arrives at a crucial time when the demand for LLM expertise is skyrocketing across industries.
What sets this book apart is its practical, end-to-end approach. By walking readers through the creation of an LLM Twin, it bridges the often daunting gap between theory and real-world application. From data engineering and model fine-tuning to advanced topics like RAG pipelines and inference optimization, the authors leave no stone unturned.
I’m particularly impressed by the emphasis on MLOps and LLMOps principles. As organizations increasingly rely on LLMs, understanding how to build scalable, reproducible, and robust systems is paramount. The inclusion of orchestration strategies and cloud integration showcases the authors’ commitment to equipping readers with truly production-ready skills.
Whether you’re a seasoned ML practitioner looking to specialize in LLMs or a software engineer aiming to break into this exciting field, this handbook provides the perfect blend of foundational knowledge and cutting-edge techniques. The clear explanations, practical examples, and focus on best practices make it an invaluable resource for anyone serious about mastering LLM engineering.
In an era where AI is reshaping industries at breakneck speed, stands out as an essential guide for navigating the complexities of large language models. It’s not just a book; it’s a roadmap to becoming a proficient LLM engineer in today’s AI-driven landscape.
The serves as an invaluable resource for anyone seeking a hands-on understanding of LLMs. Through practical examples and a comprehensive exploration of the LLM Twin project, the author effectively demystifies the complexities of building and deploying production-level LLM applications.
One of the book’s standout features is its use of the LLM Twin project as a running example. This AI character, designed to emulate the writing style of a specific individual, provides a tangible illustration of how LLMs can be applied in real-world scenarios.
The author skillfully guides readers through the essential tools and technologies required for LLM development, including Hugging Face, ZenML, Comet, Opik, MongoDB, and Qdrant. Each tool is explained in detail, making it easy for readers to understand their functions and how they can be integrated into an LLM pipeline.
LLM Engineer’s Handbook also covers a wide range of topics related to LLM development, such as data collection, fine-tuning, evaluation, inference optimization, and MLOps. Notably, the chapters on supervised fine-tuning, preference alignment, and Retrieval Augmented Generation (RAG) provide in-depth insights into these critical aspects of LLM development.
A particular strength of this book lies in its focus on practical implementation. The author excels at providing concrete examples and guidance on how to optimize inference pipelines and deploy LLMs effectively. This makes the book a valuable resource for both researchers and practitioners.
This book is highly recommended for anyone interested in learning about LLMs and their practical applications. By providing a comprehensive overview of the tools, techniques, and best practices involved in LLM development, the authors have created a valuable resource that will undoubtedly be a reference for many LLM Engineers
Contributors
About the authors
Paul Iusztin is a senior ML and MLOps engineer with over seven years of experience building GenAI, Computer Vision and MLOps solutions. His latest contribution was at Metaphysic, where he served as one of their core engineers in taking large neural networks to production. He previously worked at CoreAI, Everseen, and Continental. He is the Founder of Decoding ML, an educational channel on production-grade ML that provides posts, articles, and open-source courses to help others build real-world ML systems.
Maxime Labonne is the Head of Post-Training at Liquid AI. He holds a PhD. in ML from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. As an active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralDaredevil. He is the author of the best-selling book , published by Packt.
I want to thank my family and partner. Your unwavering support and patience made this book possible.
About the reviewer
Rany ElHousieny is an AI solutions architect and AI engineering manager with over two decades of experience in AI, NLP, and ML. Throughout his career, he has focused on the development and deployment of AI models, authoring multiple articles on AI systems architecture and ethical AI deployment. He has led groundbreaking projects at companies like Microsoft, where he spearheaded advancements in NLP and the Language Understanding Intelligent Service (LUIS). Currently, he plays a pivotal role at Clearwater Analytics, driving innovation in GenAI and AI-driven financial and investment management solutions.
I would like to thank Clearwater Analytics for providing a supportive and learning environment that fosters growth and innovation. The vision of our leaders, always staying ahead with the latest technologies, has...
