9781806678655 - architecting generative ai applications: build, deploy, and scale production-ready genai systems with llmops best practices di leonid kuligin (17 risultati)
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Paperback. Condizione: new. Paperback. Take generative AI from prototype to production with confidence, master core LLM architectures, rigorous evaluation (offline and A/B testing), LLMOps and deployment pipelines, and the reliability practices that keep systems stable, secure, and scalable in the real world.Key FeaturesTurn gen…erative AI prototypes into production-ready applicationsMaster LLM evaluation, observability, and reliability engineeringDeploy and scale AI systems using LLMOps and modern DevOps toolsBook DescriptionVibe-coding tools & coding assistants make it easy to spin up generative AI prototypes. Getting those prototypes into production is where most teams stall. This book is a practical guide to building production-ready generative AI applications that are reliable, scalable, and secure, and to understanding where traditional software best practices can clash with the realities of operating LLM-based systems.Written by a Staff AI Engineer at Google, it takes you through the full AI product lifecycle: scoping and building effective prototypes, aligning them with business goals, and scaling enterprise-wide generative AI adoption. You will learn how to evaluate LLMs with offline metrics, human-in-the-loop methods, and statistical testing. Next, you will design core architectures such as RAG, vector databases, agents, and memory systems. Next, operationalize these systems with production-grade code, robust testing, DevOps, MLOps, and LLMOps workflows, including deployment and scaling on modern LLMOps platforms. The book also covers security, Responsible AI, and modern observability and reliability for generative AI systems. By the end youll learn how to run post-launch A/B tests, maintain systems over time, and measure business impact. The focus is on durable engineering principles, so your products succeed beyond the prototype stage.What you will learnDesign offline and online evaluation strategies (including statistical A/B testing) and collect the right dataConvert AI prototypes into production-ready applications that are stable, scalable, & secureReduce maintenance effort with best practices in testing, configuration, and code readabilityImplement DevOps, MLOps, and LLMOpswhat's common and what differs across these approaches for AI systemsBuild platform teams to scale enterprise-wide generative AI adoptionDefine reliability targets using SRE principles and statistical A/B testingWho this book is forThis book is for technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. It is also ideal for engineering managers, product leaders, and technical decision-makers who need to understand how to deploy, scale, and maintain production-grade AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condizione: new. Paperback. Take generative AI from prototype to production with confidence, master core LLM architectures, rigorous evaluation (offline and A/B testing), LLMOps and deployment pipelines, and the reliability practices that keep systems stable, secure, and scalable in the real world.Key FeaturesTurn gen…erative AI prototypes into production-ready applicationsMaster LLM evaluation, observability, and reliability engineeringDeploy and scale AI systems using LLMOps and modern DevOps toolsBook DescriptionVibe-coding tools & coding assistants make it easy to spin up generative AI prototypes. Getting those prototypes into production is where most teams stall. This book is a practical guide to building production-ready generative AI applications that are reliable, scalable, and secure, and to understanding where traditional software best practices can clash with the realities of operating LLM-based systems.Written by a Staff AI Engineer at Google, it takes you through the full AI product lifecycle: scoping and building effective prototypes, aligning them with business goals, and scaling enterprise-wide generative AI adoption. You will learn how to evaluate LLMs with offline metrics, human-in-the-loop methods, and statistical testing. Next, you will design core architectures such as RAG, vector databases, agents, and memory systems. Next, operationalize these systems with production-grade code, robust testing, DevOps, MLOps, and LLMOps workflows, including deployment and scaling on modern LLMOps platforms. The book also covers security, Responsible AI, and modern observability and reliability for generative AI systems. By the end youll learn how to run post-launch A/B tests, maintain systems over time, and measure business impact. The focus is on durable engineering principles, so your products succeed beyond the prototype stage.What you will learnDesign offline and online evaluation strategies (including statistical A/B testing) and collect the right dataConvert AI prototypes into production-ready applications that are stable, scalable, & secureReduce maintenance effort with best practices in testing, configuration, and code readabilityImplement DevOps, MLOps, and LLMOpswhat's common and what differs across these approaches for AI systemsBuild platform teams to scale enterprise-wide generative AI adoptionDefine reliability targets using SRE principles and statistical A/B testingWho this book is forThis book is for technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. It is also ideal for engineering managers, product leaders, and technical decision-makers who need to understand how to deploy, scale, and maintain production-grade AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condizione: new. Paperback. Take generative AI from prototype to production with confidence, master core LLM architectures, rigorous evaluation (offline and A/B testing), LLMOps and deployment pipelines, and the reliability practices that keep systems stable, secure, and scalable in the real world.Key FeaturesTurn gen…erative AI prototypes into production-ready applicationsMaster LLM evaluation, observability, and reliability engineeringDeploy and scale AI systems using LLMOps and modern DevOps toolsBook DescriptionVibe-coding tools & coding assistants make it easy to spin up generative AI prototypes. Getting those prototypes into production is where most teams stall. This book is a practical guide to building production-ready generative AI applications that are reliable, scalable, and secure, and to understanding where traditional software best practices can clash with the realities of operating LLM-based systems.Written by a Staff AI Engineer at Google, it takes you through the full AI product lifecycle: scoping and building effective prototypes, aligning them with business goals, and scaling enterprise-wide generative AI adoption. You will learn how to evaluate LLMs with offline metrics, human-in-the-loop methods, and statistical testing. Next, you will design core architectures such as RAG, vector databases, agents, and memory systems. Next, operationalize these systems with production-grade code, robust testing, DevOps, MLOps, and LLMOps workflows, including deployment and scaling on modern LLMOps platforms. The book also covers security, Responsible AI, and modern observability and reliability for generative AI systems. By the end youll learn how to run post-launch A/B tests, maintain systems over time, and measure business impact. The focus is on durable engineering principles, so your products succeed beyond the prototype stage.What you will learnDesign offline and online evaluation strategies (including statistical A/B testing) and collect the right dataConvert AI prototypes into production-ready applications that are stable, scalable, & secureReduce maintenance effort with best practices in testing, configuration, and code readabilityImplement DevOps, MLOps, and LLMOpswhat's common and what differs across these approaches for AI systemsBuild platform teams to scale enterprise-wide generative AI adoptionDefine reliability targets using SRE principles and statistical A/B testingWho this book is forThis book is for technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. It is also ideal for engineering managers, product leaders, and technical decision-makers who need to understand how to deploy, scale, and maintain production-grade AI systems. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Take generative AI applications from prototype to production by mastering LLM architectures, evaluation strategies, LLMOps workflows, and deployment pipelines, using proven approaches to build reliable, secure, and scalable systemsFree…with your book: DRM-free PDF version + access to Packt's next-gen Reader\*Key Features:- Learn how to take generative AI apps from prototype to production- Apply evaluation, LLMOps, and SRE practices for reliable systems- Design scalable architectures using modern AI engineering patternsBook Description:Build production-ready generative AI applications by moving beyond prototypes and applying proven engineering principles. This book shows you how to design, evaluate, deploy, and scale AI systems that remain reliable, secure, and maintainable in real-world environments.Vibe-coding tools and coding assistants make it easy to create prototypes, but taking them into production is where most teams struggle. Written by a Staff AI Engineer at Google, this book guides you through scoping use cases, aligning them with business goals, and scaling generative AI adoption. You'll learn how to evaluate LLMs using offline metrics, human-in-the-loop approaches, and statistical testing, as well as how to design architectures such as RAG, vector databases, agents, and memory systems.You'll also understand how to operationalize these systems with production-grade code, testing practices, and DevOps, MLOps, and LLMOps workflows. The book covers deployment, scaling, and key considerations for security, Responsible AI, observability, and reliability.By the end of this book, you will be able to design, deploy, and maintain scalable generative AI applications, run A/B tests to measure impact, and apply durable engineering principles so your systems succeed beyond the prototype stage.\*Email sign-up and proof of purchase requiredWhat You Will Learn:- Design end-to-end generative AI product workflows- Build and evaluate AI systems with robust metrics- Implement production-ready code and testing practices- Apply LLMOps and automation for AI deployments- Architect scalable systems using modern AI patterns- Improve reliability with observability and SRE practices- Run A/B tests to measure product impact effectivelyWho this book is for:Technical leaders, AI engineers, data scientists, software engineers, and architects building generative AI applications. Engineering managers, product leaders, and decision-makers seeking to deploy, scale, and maintain production-grade AI systems will also benefit.Table of Contents- Building a Prototype- Evaluation- Key Architectures- From Prototype to Production- Moving from DevOps and MLOps to LLMOps- Deploying Your Application- Ethics and Security- Observability and Reliability- Maintaining Your Application- A/B Testing and Online Experiments.
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Taschenbuch. Condizione: Neu. Architecting Generative AI Applications | Build, deploy, and scale production-ready GenAI systems with LLMOps best practices | Leonid Kuligin | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781806678655 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gps…r[at]libri[dot]de | Anbieter: preigu Print on Demand.
