Michelangelo Gallery: Scalable Machine Learning Model Management at Uber
At Uber, we have a variety of machine learning applications including matching, pricing, recommendation, and personalization, resulting in a large number of machine learning models to manage in production. Building machine learning models is an iterative process that spans across a set of stages of a lifecycle. Michelangelo Gallery is Uber's scalable model management system to save, serve, observe, and orchestrate the flow of models across different stages of the ML lifecycle at scale. In this session, we will review the machine learning model lifecycle, present the challenges of managing models at scale in production systems, and give lessons learned/design considerations for building scalable production solutions. We will highlight case studies from Uber describing how Gallery has helped automate workflows, improve iteration velocity, and promote best practices across users.