In order to run Machine Learning (ML) models at scale three things are required: code, ML framework of your choice and a platform to run your code on. A number of ML frameworks exist such as TensorFlow, PyTorch, MXNet, Keras etc. When it comes to platforms, many of those exist too. Kubernetes is one such platform that runs containerised workloads. Kubernetes provides features that solve some of the challenges faced by data scientists. This talk will discuss how it helps with infrastructure management, auto scaling, auto recovery from failure, automated deployments, multi-tenancy and GPU offloading. This talk will also show how to run some of the ML Frameworks on Kubernetes such as Scikit-learn, PyTorch, MXNet & TensorFlow. The talk will also discuss when not to use Kubernetes to run your ML workloads.