LLM Engineer's Handbook: Master the art of engineering large language models from concept to production 305446
Код товару: 305446Паперова книга
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ISBN978-1836200079
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Бренд
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Автор
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Рік2024
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МоваАнглійська
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ІлюстраціїКольорові
“This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.”- Julien Chaumond, CTO and Co-founder, Hugging Face
“LLM Engineer's Handbook serves as an invaluable resource for anyone seeking a hands-on understanding of LLMs”- Antonio Gulli, Senior Director, Google.
Book Description
This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. It guides you through building an LLM-powered twin that’s cost-effective, scalable, and modular, moving beyond isolated Jupyter Notebooks to focus on production-grade end-to-end systems. With a hands-on approach, the book covers essential topics such as data engineering, supervised fine-tuning, and deployment. Practical approach to building the LLM twin use case will help you implement MLOps components in your projects.
The book includes clear examples, AWS implementations, and best practices for bringing LLMs into production environments. If you’re looking for a step-by-step guide, LLM Engineer’s Handbook by Paul Iusztin and Maxime Labonne is a must-read. It’s beginner-friendly yet detailed enough for professionals, offering downloadable code, real AWS use cases, and practical insights into inference optimization, preference alignment, and real-time data processing. Whether you're integrating LLMs on the cloud or scaling them in production, this book enables you with the knowledge to succeed.
What you will learn
- Implement robust data pipelines and manage LLM training cycles
- Create your own LLM and refine with the help of hands-on examples
- Get started with LLMOps by diving into core MLOps principles like IaC
- Perform supervised fine-tuning and LLM evaluation
- Deploy end-to-end LLM solutions using AWS and other tools
- Explore continuous training, monitoring, and logic automation
- Learn about RAG ingestion as well as inference and feature pipelines
Who this book is for
This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS are recommended. Whether you are new to AI or looking to enhance your skills,
This book provides comprehensive guidance on implementing LLMs in real-world scenarios.
About the Author
Paul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. Paul's determined passion and mission are to build data-intensive AI/ML products that serve the world and educate others about the process. As the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade ML, Paul has significantly enriched the engineering and MLOps community. His weekly content on ML engineering and his open-source courses focusing on end-to-end ML life cycles, such as Hands-on LLMs and LLM Twin, testify to his valuable contributions.
Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. 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 NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.
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Характеристики
- Бренд
- Автор
- КатегоріяПрограмування
- Рік2024
- Сторінок522
- Формат190х235 мм
- ОбкладинкаМ'яка
- Тип паперуОфсетний
- МоваАнглійська
- ІлюстраціїКольорові
Від видавця
“This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.”- Julien Chaumond, CTO and Co-founder, Hugging Face
“LLM Engineer's Handbook serves as an invaluable resource for anyone seeking a hands-on understanding of LLMs”- Antonio Gulli, Senior Director, Google.
Book Description
This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. It guides you through building an LLM-powered twin that’s cost-effective, scalable, and modular, moving beyond isolated Jupyter Notebooks to focus on production-grade end-to-end systems. With a hands-on approach, the book covers essential topics such as data engineering, supervised fine-tuning, and deployment. Practical approach to building the LLM twin use case will help you implement MLOps components in your projects.
The book includes clear examples, AWS implementations, and best practices for bringing LLMs into production environments. If you’re looking for a step-by-step guide, LLM Engineer’s Handbook by Paul Iusztin and Maxime Labonne is a must-read. It’s beginner-friendly yet detailed enough for professionals, offering downloadable code, real AWS use cases, and practical insights into inference optimization, preference alignment, and real-time data processing. Whether you're integrating LLMs on the cloud or scaling them in production, this book enables you with the knowledge to succeed.
What you will learn
- Implement robust data pipelines and manage LLM training cycles
- Create your own LLM and refine with the help of hands-on examples
- Get started with LLMOps by diving into core MLOps principles like IaC
- Perform supervised fine-tuning and LLM evaluation
- Deploy end-to-end LLM solutions using AWS and other tools
- Explore continuous training, monitoring, and logic automation
- Learn about RAG ingestion as well as inference and feature pipelines
Who this book is for
This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS are recommended. Whether you are new to AI or looking to enhance your skills,
This book provides comprehensive guidance on implementing LLMs in real-world scenarios.
About the Author
Paul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. Paul's determined passion and mission are to build data-intensive AI/ML products that serve the world and educate others about the process. As the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade ML, Paul has significantly enriched the engineering and MLOps community. His weekly content on ML engineering and his open-source courses focusing on end-to-end ML life cycles, such as Hands-on LLMs and LLM Twin, testify to his valuable contributions.
Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. 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 NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt.
Зміст
Table of Contents
- Understanding the LLM Twin Concept and Architecture
- Tooling and Installation
- Data Engineering
- RAG Feature Pipeline
- Supervised Fine-tuning
- Fine-tuning with Preference Alignment
- Evaluating LLMs
- Inference Optimization
- RAG Inference Pipeline
- Inference Pipeline Deployment
- MLOps and LLMOps
- Appendix: MLOps Principles
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