This talk will introduce the workflows and concerns of data scientists and machine learning engineers and demonstrate how to make Kubernetes a powerhouse for intelligent applications. We’ll show how community projects like Kubeflow and radanalytics.io support the entire intelligent application development lifecycle. We’ll cover several key benefits of Kubernetes for a data scientist’s workflow, from experiment design to publishing results. You’ll see how well scale-out data processing frameworks like Apache Spark work in Kubernetes. System operators will learn how Kubernetes can support data science and machine learning workflows. Application developers will learn how Kubernetes can enable intelligent applications and cross-functional collaboration. Data scientists will leave with concrete suggestions for how to use Kubernetes and open-source tools to make their work more productive.