{"product_id":"llms-in-production-christopher-brousseau-matt-sharp","title":"LLMs in Production by Christopher Brousseau \u0026 Matt Sharp | LLMOps, AI Engineering \u0026 Machine Learning","description":"\u003ch3\u003e\u003cspan\u003eLLMs in Production by Christopher Brousseau \u0026amp; Matt Sharp\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eLLMs in Production: From Language Models to Successful Products\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is a practical guide for developers, data scientists, machine learning engineers, and technology professionals who want to move Large Language Models (LLMs) from experimentation into reliable, scalable, real-world applications. Written by Christopher Brousseau and Matt Sharp, this book focuses on the engineering, deployment, evaluation, optimization, and operational challenges involved in putting modern language models into production.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eRather than concentrating only on the theory behind artificial intelligence, the book explores the application layer of foundation models and explains how organizations can design, build, deploy, monitor, and improve LLM-powered systems. It covers the practical decisions that arise when working with large models, including choosing between existing models and building custom ones, preparing suitable datasets, controlling infrastructure and computing costs, evaluating model performance, improving prompts, and maintaining security.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eAbout This Book\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLLMs introduce challenges that differ from traditional software and conventional machine learning systems. Their large size, computational requirements, data dependencies, and behavior make careful planning essential. \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eLLMs in Production\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e provides an LLMOps-oriented approach for taking AI applications from initial design through production deployment.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book explains how to build an effective platform for LLM workloads, work with foundation models, fine-tune existing models, and create applications that take advantage of language-model capabilities while addressing their limitations.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReaders also learn how to approach cost-efficient training, retraining, load testing, deployment architectures, and model optimization for different hardware environments.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eWhat You Will Learn\u003c\/span\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand the fundamentals of Large Language Models and foundation models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDecide when to use a pre-trained LLM and when to build or train your own\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDesign an effective LLMOps and machine learning platform\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrepare datasets suitable for LLM training and fine-tuning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTrain foundation models and fine-tune existing language models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eWork with parameter-efficient techniques such as PEFT and LoRA\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eExplore reinforcement learning with human feedback\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelop effective prompt-engineering strategies\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEvaluate LLM performance using practical benchmarks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eBalance model performance, infrastructure requirements, and cost\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRetrain and load-test LLM applications\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDeploy LLM applications to cloud environments\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eUnderstand deployment using Kubernetes\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eOptimize models for commodity and edge hardware\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eBuild AI-powered developer tools and applications\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDeploy smaller language models to resource-constrained devices\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAddress security and operational considerations in production AI systems\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003e\u003cspan\u003eLLMOps and Production AI\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eA major focus of the book is the transition from an experimental LLM project to a production-ready AI application. This includes understanding application architecture, data pipelines, compute requirements, model lifecycle management, monitoring, testing, security, and cost management.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe authors explain why LLM applications require a different operational mindset from conventional software. Because large models can be expensive to train and difficult to modify, decisions concerning data, infrastructure, model selection, evaluation, and deployment can have significant long-term consequences.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eTraining, Fine-Tuning and Model Optimization\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book provides practical guidance on training and adapting language models. Readers can explore the process of working with training datasets, foundation models, and fine-tuning strategies designed to make models more useful for particular applications.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eTechniques such as \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eLoRA and PEFT\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e are covered as approaches for making model adaptation more efficient. The book also discusses reinforcement learning with human feedback and other techniques relevant to improving model behavior.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eBuilding LLM Applications\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eBeyond model development, \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eLLMs in Production\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e focuses on creating applications that use language models effectively. It examines how developers can integrate LLM capabilities into software while designing around their limitations.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eExample projects help connect concepts to practical implementation, including building a custom LLM, developing an AI coding extension for VS Code, and deploying a smaller model to a Raspberry Pi.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eDeployment and Infrastructure\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eProduction LLM systems can require substantial computing and infrastructure resources. This book explores strategies for scaling machine learning platforms, deploying models to cloud environments, and optimizing models for available hardware.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eIt also covers deployment considerations involving \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eKubernetes\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, commodity hardware, and edge devices, making the material relevant to engineers responsible for taking AI systems beyond local development environments.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eEvaluation, Cost and Reliability\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eSuccessful AI products require more than an impressive model. Production systems must be evaluated, tested, monitored, and optimized.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book discusses practical topics including:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eModel evaluation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eIndustry benchmarks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLoad testing\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRetraining\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCost and performance optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eInfrastructure planning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSecurity\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eProduction deployment\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel limitations\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eApplication reliability\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThese topics help readers think about LLMs as complete software and machine-learning systems rather than isolated AI models.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eKey Topics Covered\u003c\/span\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eLarge Language Models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGenerative AI\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eFoundation Models\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLLMOps\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMLOps\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMachine Learning Engineering\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNLP and Natural Language Processing\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLLM Training\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eFine-Tuning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLoRA\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePEFT\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eRLHF\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePrompt Engineering\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel Evaluation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAI Application Development\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eKubernetes\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCloud Deployment\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEdge AI\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel Optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLoad Testing\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAI Infrastructure\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eProduction Machine Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAI Security\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eCost Optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003e\u003cspan\u003eWhy Read This Book?\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eLLMs in Production\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is especially valuable for readers who already understand Python and basic cloud deployment and want to learn how modern language-model applications are engineered for real-world use. Rather than treating LLMs purely as an academic subject, the book emphasizes practical production concerns and the decisions engineers face when deploying AI systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eIt provides a bridge between understanding what LLMs are and understanding how to successfully use them as part of production software. The combination of LLM fundamentals, application development, infrastructure, fine-tuning, evaluation, deployment, and operational considerations makes it a useful reference for modern AI engineering.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eWho Should Read This?\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis book is particularly suitable for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eData scientists\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMachine learning engineers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAI engineers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eSoftware developers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMLOps engineers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevOps and cloud engineers working with AI\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePython developers exploring LLM applications\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTechnology professionals building generative AI products\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eStudents studying modern machine learning engineering\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers interested in LLMOps and production AI\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eTeams responsible for deploying and maintaining AI applications\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe publisher specifically positions the book for \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003edata scientists and ML engineers who know Python and the basics of cloud deployment\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eProduct Details\u003c\/span\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eBook Title:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e LLMs in Production: From Language Models to Successful Products\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eAuthors:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Christopher Brousseau \u0026amp; Matt Sharp\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePublisher:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Manning\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eLanguage:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e English\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eGenre:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Artificial Intelligence \/ Machine Learning \/ Software Engineering\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePublication:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e 2025\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eLength:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e 456 pages\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePrint ISBN-13:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e 9781633437203\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eFormat:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e English Paperback \/ Trade Paperback\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eLevel:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Intermediate to Advanced\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePrimary Topics:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e LLMs, LLMOps, MLOps, AI Engineering, NLP, Machine Learning\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003ePublisher records confirm the 2025 Manning edition, 456-page length, and ISBN 9781633437203.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eAbout the Authors\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eChristopher Brousseau\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is a Staff Machine Learning Engineer with a background in linguistics and localization. His work specializes in linguistically informed natural language processing and ML\/data product initiatives.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eMatt Sharp\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is an engineer, former data scientist, and experienced technology leader specializing in MLOps and the deployment, management, and scaling of machine learning models in production.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eFinal Overview\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cspan\u003eIf you are looking for a practical book on \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003ebuilding and deploying LLM-powered applications\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, this is a strong resource for understanding the engineering challenges behind production AI. From model selection and training to fine-tuning, evaluation, infrastructure, deployment, optimization, security, and cost management, \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eLLMs in Production\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e provides a practical roadmap for turning language-model technology into useful and scalable products.\u003c\/span\u003e\u003c\/p\u003e","brand":"BookBeen","offers":[{"title":"Default Title","offer_id":53462424912181,"sku":null,"price":1349.0,"currency_code":"PKR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0972\/1731\/5125\/files\/BookBeen-2026-09-04T210902.502.png?v=1788538252","url":"https:\/\/bookbeen.com\/products\/llms-in-production-christopher-brousseau-matt-sharp","provider":"Bookbeen","version":"1.0","type":"link"}