ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize and manage models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this talk, I’ll present MLflow, a new open source project from Databricks that aims to design an *open* ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, letting ML teams use the best libraries, deployment tools and public clouds with minimal effort. I’ll show how MLflow streamlines the ML development process whether you are a single data scientist working alone or part of a 100,000 person organization.