More and more applications are powered by Machine Learning (ML) models. Where the gap between Software Engineers and a Production environment on Kubernetes is already big, the gap between Data Scientists and that same production environment is enormous. In this talk, we will provide you with a framework for translating ML requirements into infrastructural requirements and concrete Kubernetes resources. In the first half of this talk, we will discuss how ML applications are different from most other applications, how ML workloads are structured and how ML requirements translate into Kubernetes resource configurations. In the second half of the talk, we will put this theory into practice. We will do a live demonstration of an ML Deployment on Kubernetes using Istio, Knative and Kubeflow Serving.