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.