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 unlock AI innovation with the power of cloud native infrastructure. Authors Roland Hu 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. n 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. n n- Learn to run GenAI models on Kubernetes for efficient scalability n- Get techniques to train and fine-tune LLMs within Kubernetes environments n- See how to deploy production-ready AI systems with automation and resource optimization n- Discover how to monitor and scale GenAI applications to handle real-world demand n- Uncover the best tools to operationalize your GenAI workloads n- Learn how to run agent-based and AI-driven applications