LLM Engineer's Handbook
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 — AI Researcher, LLM Expert, and Author
Maxime Labonne is a leading machine learning researcher and AI expert, currently serving as 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 (GDE) in AI/ML.
Expertise in LLMs and AI Development
Maxime Labonne specializes in:
- Large Language Models (LLMs)
- Model fine-tuning and optimization
- Post-training techniques for AI systems
- Evaluation and benchmarking of AI models
His work focuses on making advanced AI systems more efficient, reliable, and accessible.
Open-Source Contributions and Educational Content
Maxime has made significant contributions to the open-source AI community, including:
- The LLM Course — a structured learning resource for mastering large language models
- In-depth guides on fine-tuning AI models
- Tools like LLM AutoEval for evaluating model performance
These resources are widely used by developers and researchers to build and improve AI systems.
Paul Iusztin
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.
Books
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| 1 | LLM Engineer's Handbook |