Today, machine learning plays a key role in Uber’s business, being used to make business critical decisions across the board from marketplace pricing, Eats search and discovery, maps ETA, fraud detection etc. Michelangelo is an end-to-end ML platform that democratizes machine learning and enables ML practitioners to seamlessly build, deploy, and operate machine learning solutions at Uber’s scale.
In this talk, we will discuss how Michelangelo addresses the challenges of streamlining ML developer experience with seamless UI and code driven model iteration, supporting large-scale deep learning with a declarative ML application framework, and consolidating fragmented ecosystems with a unified API framework inspired by Kubernetes CRD design pattern.
While Michelangelo had some industry leading features like Horovod, Palette feature store etc, the ML industry is rapidly evolving. We believe that the future of Michelangelo is an open ML platform that leverages the best-of-class 3rd party or in-house ML components. We will share some early experience on the plug-and-play of ML components in Michelangelo by using our unified API framework.