Michelangelo is the Machine Learning platform that we have built at Uber. The purpose of Michelangelo is to enable data scientists and engineers (and eventually non-technical users) to easily build, deploy, and operate machine learning solutions at scale. It is designed to be ML-as-a-service, covering the end-to-end machine learning workflow: manage data, train models, evaluate models, deploy models, make predictions, and monitor predictions. Michelangelo supports traditional ML models, time series forecasting, and deep learning. In this talk, I will use one of our models, the UberEATS estimated delivery time model, as a case study to illustrate how the system works end-to-end. I will also cover some of the lessons we learned while developing and scaling the platform.