Michael Mui

If you have read our previous article, ML Education at Uber: Frameworks Inspired by Engineering Principles, you have seen several examples of how Uber benefits from applying Engineering Principles to drive the ML Education Program’s content design and program frameworks. In this follow-up, we will dig deeper into what we believe to be other unique aspects of ML Education at Uber: our approach to Content Components, Content Delivery, Observability, and Marketing & Reach.

At Uber, millions of machine learning (ML) predictions are made every second, and hundreds of applied scientists, engineers, product managers, and researchers work on ML solutions daily. Uber wins by scaling machine learning. We recognize org-wide that a powerful way to scale machine learning adoption is by educating.

uber Michael Mui Michelangelo, Uber’s machine learning (ML) platform, powers machine learning model training across various use cases at Uber, such as forecasting rider demand, fraud detection, food discovery and recommendation for Uber Eats, and improving the accuracy of estimated times of arrival (ETAs). As Michelangelo’s increasingly deep tree models create larger data sets, the efficient training of distributed gradient boosting (GBD) algorithms becomes evermore challenging.