GenAI on Kubernetes: training, inference and serving tutorial

Kubernetes is quickly becoming the preferred platform for running GenAI workloads, but wiring together training jobs, inference pipelines, and scalable model serving can feel overwhelming. This hands-on tutorial walks you through the full lifecycle of GenAI on Kubernetes—from launching distributed training jobs with GPU scheduling, to running optimized inference workloads, to exposing production-grade model endpoints using cloud-native serving stacks. We’ll cover key building blocks like operators, autoscaling (HPA/KEDA), vector stores, model registries, and popular OSS frameworks (KServe, Ray, vLLM, Kubeflow, and more). You’ll learn how to design resource-efficient GPU clusters, fine-tune models securely, and deploy multi-model serving architectures that behave predictably under real traffic. By the end, you’ll have a working reference setup and a clear understanding of how to operationalize GenAI workloads using the Kubernetes patterns you already know. No magic—just practical, reproducible workflows you can take back to your platform today.