000			02086nam a22002537a 4500
005			20260710164537.0
008			260710s2024 |||||||| |||| 00| 0 eng d
020			_a9781836200079
040			_cPK-LaUMT
082			_a006.332 _bLUS-L
100	1		_aIusztin, Paul _913569
245	1	0	_aLLM engineer's handbook : _bmaster the art of engineering large language models from concept to production / _cPaul Iusztin and Maxime Labonne
260			_aBirmingham : _bPackt Publishing, _c2024
300			_axxvi, 490 p.
490			_aExpert insight
500			_aIndex present
520			_aThe 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
546			_aEng
650			_aMachine learning
650			_aDeep learning
650			_aComputer software development
700			_aLabonne, Maxime _913570
942			_cBK
999			_c141384 _d141384