Generative AI on Kubernetes Operationalizing Large Language Models

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