Kubernetes as a platform is being asked to support an ever increasing range of workloads, including machine learning and big data processing. These new workloads introduce challenges both for both end users and cluster administrators. Data scientists want the flexibility to run any workload and library they require, data engineers want to ensure the scalability and reliability of production workloads, and cluster administrators want to maintain governance and control over cluster resources. What’s needed is a machine learning platform on Kubernetes that seeks to balance these competing objectives. In this talk, we will share lessons learned from enterprise customers, and our vision of the road ahead for machine learning and AI on Kubernetes.