Michelangelo: Uber's machine learning platform - Achal Shah

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

[@RichardSimoes86]

Skip to min 8:00 for the intro to the platform

[@kunalr_ai]

Is this same framework become Ludwig?

[@moef.5326]

There are many problems with this. Just one, for example, when I "pick up" an order from McDonald's, Uber doesn't actually know the order's ready , because the employees don't tell the app. Therefore, I can sit in the parking lot (and do) for up to 45 minutes, and get paid, and the AI has no idea that I've already picked up the food 45 minutes ago. Instead of making $7.25 for this delivery, I get $20.