We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
Uber Engineering Manager and open source software community member Felix Cheung talks about his work with the Apache Software Foundation, open source at Uber, and XGBoost, a machine learning library for optimized distributed gradient boosting.
Censored time-to-event data is critical to the proper modeling and understanding of customer engagement on the Uber platform. In this article, we demonstrate an easier way to model this data using Pyro.
Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.