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LLM engineer's handbook :master the art of engineering large language models from concept to production /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	LLM engineer's handbook :Paul Iusztin, Maxime Labonne.
其他題名:	master the art of engineering large language models from concept to production /
其他題名:	Large language models engineer's handbook
作者:	Iusztin, Paul,
其他作者:	Labonne, Maxime,
出版者:	Birmingham, UK :Packt Publishing Ltd.,2024.
面頁冊數:	xxvi, 492 p. :col. ill. ;24 cm.
標題:	Natural language processing (Computer science)
ISBN:	9781836200079 (pbk.) :

LLM engineer's handbook :master the art of engineering large language models from concept to production /
Iusztin, Paul,

LLM engineer's handbook :master the art of engineering large language models from concept to production /Large language models engineer's handbookPaul Iusztin, Maxime Labonne. - Birmingham, UK :Packt Publishing Ltd.,2024. - xxvi, 492 p. :col. ill. ;24 cm. - Expert insight. - Expert insight..

Includes bibliographical references and index.

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.

The field of Artificial Intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. This comprehensive guide walks you through building an end-to-end LLM-powered technical content writer, by overcoming isolated Jupyter Notebooks and focusing on teaching 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, combined with detailed examples, helps you understand the implementation of MLOps components in your projects. The book also explores the cutting-edge advancements in the field, including inference optimization and real-time data processing, making it a vital resource for anyone looking to leverage LLMs in their projects. By the end of this book, you will be proficient in deploying robust large language models, leveraging them to solve practical problems, and maintaining low-latency and high-availability inference capabilities. Whether you are new to AI or an experienced practitioner, this book offers valuable insights and practical knowledge to enhance your expertise in LLMs.

ISBN: 9781836200079 (pbk.) :NT$1985

Standard No.: CIPO000127894

Nat. Bib. No.: GBC4D0567bnb

Nat. Bib. Agency Control No.: 300312027UkSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

LLM engineer's handbook :master the art of engineering large language models from concept to production /
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520 $a The field of Artificial Intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. This comprehensive guide walks you through building an end-to-end LLM-powered technical content writer, by overcoming isolated Jupyter Notebooks and focusing on teaching 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, combined with detailed examples, helps you understand the implementation of MLOps components in your projects. The book also explores the cutting-edge advancements in the field, including inference optimization and real-time data processing, making it a vital resource for anyone looking to leverage LLMs in their projects. By the end of this book, you will be proficient in deploying robust large language models, leveraging them to solve practical problems, and maintaining low-latency and high-availability inference capabilities. Whether you are new to AI or an experienced practitioner, this book offers valuable insights and practical knowledge to enhance your expertise in LLMs.
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