Over the past few years, we have seen leaps and strides in ML and most recently generative AI. Companies and software teams are rushing to enhance, rebuild, and create new software offerings with this new intelligence. As they innovate and create delightful new experiences for their customers new challenges arise. Understanding how these applications work and how to use state-of-the-art infrastructure tools like Kubernetes will help organizations and professionals succeed with this new technology.The book covers essential technical implementations from ML fundamentals through advanced deployment strategies, focusing on practical patterns. Core topics include Kubernetes-native GPU scheduling and resource management, MLOps pipeline architectures using Kubeflow/MLflow, and advanced model serving patterns. It details data management architectures, vector databases, and RAG systems, alongside monitoring solutions with Prometheus/Grafana. Finally, we will look at some advanced concerns for production in the realm of security and data reliability. After reading this book, you will be equipped with a broad knowledge of the end-to-end generative AI pipeline and how Kubernetes can be leveraged to run your generative AI workloads at scale in the real-world.