Learn how to put Large Language Model-based applications into production safely and efficiently.
Large Language Models (LLMs) are the foundation of AI tools like ChatGPT, LLAMA and Bard. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. In LLMs in Production you will:
LLMs in Production delivers vital insights into delivering MLOps for LLMs. You'll learn how to operationalize these powerful AI models for chatbots, coding assistants, and more. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice.
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Christopher Brousseau is a Staff MLE at JPMorganChase with a linguistics and localization background. He specializes in linguistically-informed NLP, especially with an international focus and has led successful ML and Data product initiatives at both startups and Fortune 500s.
Matt Sharp is an engineer, former data scientist, and seasoned technology leader in MLOps. Has led many successful data initiatives for both startups and top-tier tech companies alike. Matt specializes in deploying, managing, and scaling machine learning models in production, regardless of what that production setting looks like.
From the back cover:
LLMs in Production is the comprehensive guide to LLMs you'll need to effectively guide you to production usage. It takes you through the entire lifecycle of an LLM, from initial concept, to creation and fine tuning, all the way to deployment. You'll discover how to effectively prepare an LLM dataset, cost-efficient training techniques like LORA and RLHF, and how to evaluate your models against industry benchmarks.
Learn to properly establish deployment infrastructure and address common challenges like retraining and load testing. Finally, you'll go hands-on with three exciting example projects: a cloud-based LLM chatbot, a Code Completion VSCode Extension, and deploying LLM to edge devices like Raspberry Pi. By the time you're done reading, you'll be ready to start developing LLMs and effectively incorporating them into software.
About the reader:
For data scientists and ML engineers, who know Python and the basics of Kubernetes and cloud deployment.
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Zustand: Bueno. : Este libro práctico ofrece explicaciones claras y detalladas sobre cómo funcionan los modelos de lenguaje de gran tamaño (LLM) y cómo integrarlos de manera efectiva en aplicaciones reales. A través de ejemplos enriquecedores, los autores guían al lector desde los fundamentos tecnológicos hasta el despliegue en producción, cubriendo aspectos críticos como MLOps, ingeniería de datos y evaluación de modelos.Diseñado para científicos de datos e ingenieros de aprendizaje automático, el texto aborda desafíos prácticos como el ajuste fino (fine-tuning), la optimización de costes y el rendimiento en hardware comercial. Incluye proyectos prácticos que van desde la creación de un asistente de código hasta el despliegue de modelos en dispositivos de borde como Raspberry Pi, proporcionando una hoja de ruta completa para transformar modelos teóricos en productos exitosos. EAN: 9781633437203 Tipo: Libros Categoría: Tecnología|Ciencias Título: LLMs in Production: From Language Models to Successful Products Autor: Christopher Brousseau| Matt Sharp Editorial: Manning Publications Idioma: en Páginas: 456 Formato: tapa blanda. Artikel-Nr. Happ-2026-03-12-0c972c12
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Taschenbuch. Zustand: Neu. Neuware - From the back cover: LLMs in Production is the comprehensive guide to LLMs you'll need to effectively guide you to production usage. It takes you through the entire lifecycle of an LLM, from initial concept, to creation and fine tuning, all the way to deployment. You'll discover how to effectively prepare an LLM dataset, cost-efficient training techniques like LORA and RLHF, and how to evaluate your models against industry benchmarks. Learn to properly establish deployment infrastructure and address common challenges like retraining and load testing. Finally, you'll go hands-on with three exciting example projects: a cloud-based LLM chatbot, a Code Completion VSCode Extension, and deploying LLM to edge devices like Raspberry Pi. By the time you're done reading, you'll be ready to start developing LLMs and effectively incorporating them into software. About the reader: For data scientists and ML engineers, who know Python and the basics of Kubernetes and cloud deployment. Artikel-Nr. 9781633437203
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