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Presentation: Michelangelo: Uber’s Machine Learning Platform

Track: The Practice & Frontiers of AI

Location: Ballroom BC

Day of week: Monday

Level: Intermediate

Persona: Architect, ML Engineer, Technical Engineering Manager

Abstract

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.

Speaker: Jeremy Hermann

ML Platform Team @Uber

Jeremy Hermann leads the Machine Learning Platform team at Uber. Before Uber, he led engineering and data science teams at a number of Bay Area startups, focused on machine learning, data infrastructure, and security.

Find Jeremy Hermann at

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