Llms in Production : From Language Models to Successful Products

Brousseau, Christopher; Sharp, Matt

ISBN 10: 1633437205 / ISBN 13: 9781633437203

Edité par Manning, 2025

A propos de cet article

N° de réf. du vendeur 48267387-n

Détails bibliographiques

• Titre: Llms in Production : From Language Models to Successful Products
• Éditeur: Manning
• Date de parution: 2025
• Langue: anglais
• ISBN à 10 chiffres: 1633437205
• ISBN à 13 chiffres: 9781633437203
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

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:

• Grasp the fundamentals of LLMs and the technology behind them
• Evaluate when to use a premade LLM and when to build your own
• Efficiently scale up an ML platform to handle the needs of LLMs
• Train LLM foundation models and finetune an existing LLM
• Deploy LLMs to the cloud and edge devices using complex architectures like RLHF
• Build applications leveraging the strengths of LLMs while mitigating their weaknesses

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.

À propos de l?auteur

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.