There is a pressing need for tools and workflows that meet data scientists where they are. This is also a serious business need: How to enable an organization of data scientists, who are not software engineers by training, to build and deploy end-to-end machine learning workflows and applications independently? In this talk, we discuss the problem space and the approach we took to solving it at Netflix. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.