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.
Nader Azari is a software engineer on the Marketplace Forecasting team and a contributor to the Michelangelo Machine Learning platform at Uber. He works on building production level systems for spatiotemporal forecasting, developing tooling to scale ML workflows, and improving model iteration velocity.
Yifan Ma is a software engineer at Uber machine learning platform team working on ML workflow automation. Previously, he was a professional mountaineer and reached the summit of Mount Everest.
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