Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.
We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.
Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.
To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.
From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox
Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.
Uber’s Sensing, Inference, and Research team released a software upgrade for GPS on Android phones that significantly improves location accuracy in urban environments.
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
In this article, members of Uber Bangalore Engineering discuss their role in building reliable transportation systems at scale for India—and beyond.
Uber Engineering’s Data Visualization team uses their deck.gl and Voyager visualization platforms to map rider behavior during the August 21, 2017 solar eclipse.
Uber Engineering debuts deck.gl 4.0, the latest version of our open source data visualization framework featuring enhanced geospatial exploration, a re-architected codebase, and more comprehensive documentation.