The Fifth Elephant 2018

Uber Engineering is committed to developing technologies that create seamless, impactful experiences for our customers. We are increasingly investing in Machine Learning to fulfill this vision. At Uber, our contribution to this space is Michelangelo, an internal ML-as-a-service platform that democratizes machine learning and makes scaling AI to meet the needs of the business as easy as requesting a ride.

In this talk, I’ll go over some of Uber’s early challenges at applying ML at scale, and the context around which Michelangleo was born. We’ll also talk about what the Michelangelo system looks like, and some important components that aim to lower the bar on applying ML at Uber.

Outline

• ML Use cases at Uber
• Early Challenges to applying Machine Learning at Scale
• Productionizing ML
• An end-to-end look at Michelangelo
• Key components
• What’s next

Speaker bio

Sr. Software Engineer working on Michelangelo, and Deep Learning infrastructure