Building Reliable Generative AI Systems on Kubernetes (Paperback)
Idioma: inglés
Editorial: Independently Published, 2026
- Tapa blanda
- Nuevo
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de AbeBooks desde el 12 de octubre de 2005
Condición: Nuevo
EUR 27,46
Cantidad disponible: 1 disponible
Añadir al carritoDescripción del artículo del vendedor
Paperback. Your LLM application works perfectly in development. Then real users arrive.Suddenly, latency increases. GPUs become overloaded. Requests pile up. Costs rise unexpectedly. Deployments fail under pressure. What looked simple in a testing environment becomes a complex infrastructure challenge in production.Building Reliable Generative AI Systems on Kubernetes Without Deployment Failures provides a practical blueprint for designing, deploying, scaling, and operating stable Large Language Model (LLM) inference systems in real-world environments.This book explains how to build production-ready Generative AI infrastructure using Kubernetes, KServe, Ray, GPU orchestration, modern inference engines, and advanced traffic management strategies. Instead of focusing only on models, it focuses on the engineering systems required to keep AI services reliable, efficient, and predictable at scale.You will learn how to design LLM platforms that handle demanding workloads, avoid common deployment failures, optimize GPU usage, and maintain consistent performance even as traffic and complexity increase.Inside this book, you will discover how to: Design production-grade LLM inference architectures on KubernetesBuild reliable AI serving pipelines using KServe and distributed inference frameworksOptimize GPU allocation, scheduling, and resource managementUnderstand vLLM, TensorRT-LLM, and modern inference runtime strategiesImprove latency, throughput, and scalability in production AI systemsManage multi-tenant GPU environments without performance conflictsImplement traffic engineering with AI gateways, routing policies, and request prioritizationMonitor GPU performance, inference latency, and infrastructure costsTroubleshoot common failures in large-scale GenAI deploymentsBuild enterprise-ready AI platforms designed for reliability and efficiencyWhether you are a cloud engineer, DevOps professional, machine learning engineer, platform engineer, or technical leader building Generative AI solutions, this book gives you the practical systems knowledge needed to move beyond prototypes and create dependable AI services that operate successfully in production.Generative AI infrastructure is becoming the foundation of the next generation of software systems. The teams that understand how to engineer reliable LLM platforms will have the ability to build faster, scale smarter, and operate AI services with confidence.Start building production-ready Generative AI systems today. Get your copy of Building Reliable Generative AI Systems on Kubernetes Without Deployment Failures and learn how to design stable, scalable, and efficient AI infrastructure that performs when it matters most. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de ref. del artículo 9798186477884
- Título
- Building Reliable Generative AI Systems on Kubernetes (Paperback)
- Autor
- Jefferson C. Phillips
- Editorial
- Independently Published
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 13
- 9798186477884
Your LLM application works perfectly in development. Then real users arrive.
Suddenly, latency increases. GPUs become overloaded. Requests pile up. Costs rise unexpectedly. Deployments fail under pressure. What looked simple in a testing environment becomes a complex infrastructure challenge in production.
Building Reliable Generative AI Systems on Kubernetes Without Deployment Failures provides a practical blueprint for designing, deploying, scaling, and operating stable Large Language Model (LLM) inference systems in real-world environments.
This book explains how to build production-ready Generative AI infrastructure using Kubernetes, KServe, Ray, GPU orchestration, modern inference engines, and advanced traffic management strategies. Instead of focusing only on models, it focuses on the engineering systems required to keep AI services reliable, efficient, and predictable at scale.
You will learn how to design LLM platforms that handle demanding workloads, avoid common deployment failures, optimize GPU usage, and maintain consistent performance even as traffic and complexity increase.
Inside this book, you will discover how to:
-
Design production-grade LLM inference architectures on Kubernetes
-
Build reliable AI serving pipelines using KServe and distributed inference frameworks
-
Optimize GPU allocation, scheduling, and resource management
-
Understand vLLM, TensorRT-LLM, and modern inference runtime strategies
-
Improve latency, throughput, and scalability in production AI systems
-
Manage multi-tenant GPU environments without performance conflicts
-
Implement traffic engineering with AI gateways, routing policies, and request prioritization
-
Monitor GPU performance, inference latency, and infrastructure costs
-
Troubleshoot common failures in large-scale GenAI deployments
-
Build enterprise-ready AI platforms designed for reliability and efficiency
Whether you are a cloud engineer, DevOps professional, machine learning engineer, platform engineer, or technical leader building Generative AI solutions, this book gives you the practical systems knowledge needed to move beyond prototypes and create dependable AI services that operate successfully in production.
Generative AI infrastructure is becoming the foundation of the next generation of software systems. The teams that understand how to engineer reliable LLM platforms will have the ability to build faster, scale smarter, and operate AI services with confidence.
Start building production-ready Generative AI systems today. Get your copy of Building Reliable Generative AI Systems on Kubernetes Without Deployment Failures and learn how to design stable, scalable, and efficient AI infrastructure that performs when it matters most.
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