Paperback or Softback. Condici�n: New. LLM Engineer's Handbook: Master the art of engineering large language models from concept to production 1.96. Book. N� de ref. del art�culo: BBS-9781836200079

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LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

Paul Iusztin; Maxime Labonne

Publicado por Packt Publishing, 2024

ISBN 10: 1836200072 ISBN 13: 9781836200079

Nuevo Tapa blanda

Librería: California Books, Miami, FL, Estados Unidos de America

Calificaci�n del vendedor: 5 de 5 estrellas

Condici�n: New. N� de ref. del art�culo: I-9781836200079

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LLM Engineer's Handbook (Paperback)

Paul Iusztin

Publicado por Packt Publishing Limited, Birmingham, 2024

ISBN 10: 1836200072 ISBN 13: 9781836200079

Nuevo Paperback

Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America

Calificaci�n del vendedor: 5 de 5 estrellas

Paperback. Condici�n: new. Paperback. Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practicesKey FeaturesBuild and refine LLMs step by step, covering data preparation, RAG, and fine-tuningLearn essential skills for deploying and monitoring LLMs, ensuring optimal performance in productionUtilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applicationsBook DescriptionArtificial 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 thats 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.What you will learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine it with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoringPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsDesign scalable and modularLLM systemsLearn about RAG applications by building a feature and inference pipeline.Who this book is forThis 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 is 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 Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de ref. del art�culo: 9781836200079

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