Generative AI on Kubernetes

Roland Huss, Daniele Zonca

ISBN 10: 1098171926 / ISBN 13: 9781098171926

Published by O'Reilly Media, US, 2026

About this Item

Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure. Authors Roland Huss and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively.Learn to run GenAI models on Kubernetes for efficient scalabilityGet techniques to train and fine-tune LLMs within Kubernetes environmentsSee how to deploy production-ready AI systems with automation and resource optimizationDiscover how to monitor and scale GenAI applications to handle real-world demandUncover the best tools to operationalize your GenAI workloadsLearn how to run agent-based and AI-driven applications.

Seller Inventory # LU-9781098171926

Bibliographic Details

• Title: Generative AI on Kubernetes
• Publisher: O'Reilly Media, US
• Publication Date: 2026
• Language: English
• ISBN 10: 1098171926
• ISBN 13: 9781098171926
• Binding: Paperback
• Condition: New
• Dimensions: 17.78W x 5.08H x 23.34D centimeters 17.78 centimeters width by 5.08 centimeters height by 23.34 centimeters depth
• Weight: 644 grams

About this title

Synopsis

Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to combine AI innovation with the power of cloud native infrastructure. Authors Roland Huß and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.

With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively.

• Learn how to deploy LLMs more efficiently with optimized inference runtimes
• Get hands-on with GPU scheduling, including hardware detection and multinode scaling
• Monitor and understand LLM-specific metrics like Time to First Token and token throughput
• Know when to fine-tune a model or when retrieval augmentation is the better choice
• Discover how to evaluate models with standardized benchmarks before committing GPU resources
• Learn to run agentic applications with secure tool integration, identity management, and persistent state

About the Authors

Dr. Roland Huss is a seasoned software engineer with over 25 years of experience in the field. Currently working at Red Hat, he is the architect of OpenShift Serverless and a former member of the Knative TOC. Roland is a passionate Java and Golang coder and a sought-after speaker at tech conferences. An advocate of open source, he is an active contributor and enjoys growing chili peppers in his free time.

Daniele Zonca is a Senior Principal Software Engineer and Architect for model serving of Red Hat OpenShift AI, Red Hat's flagship AI product combining multiple stacks.