This document discusses Uber's machine learning platform called Michelangelo. It provides an overview of how ML is used across Uber for applications like ETAs, Uber Eats, autonomous vehicles, and more. It describes the goals and key components of the Michelangelo platform, including a feature store, scalable training, partitioned models, visualization tools, and a sharded deployment architecture. The presentation concludes by discussing next steps like adding Python support and continuous learning capabilities.