With the release of deck.gl version 7.3, Uber’s open source visualization tool now supports rendering massive geospatial data sets formatted according to the OGC 3D Tiles community standard.
Uber's IT Engineering team builds the tools and systems that help other Uber employees do their jobs. Meet a few of these remarkable behind-the-scenes engineers.
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
Uber has embraced Presto, a high performance, distributed SQL query engine, and joined the Presto Foundation. Meet the Uber engineers who contribute to and use Presto on a daily basis.
When developing Uber's self driving car systems, engineers found a way to identify edge case scenarios amongst terabytes of sensor data representing real-world situations.
CTO Thuan Pham sat down with former intern, now employee, Sudhanshu Mishra to talk about his early experiences in the technology industry and growing Uber.
Uber is honored to join the Presto Foundation, a new initiative hosted by the Linux Foundation, to advance the open source data processing community.
Uber introduces Hypothesis GU Func, a new extension to Hypothesis, as an open source Python package for unit testing.
Gaining Confidence and Improving Android Developer Workflows as a Software Engineering Intern at Uber
Ankit Agrawal reflects on his internship with Uber Engineering, working on the Developer Experience team to build a feature that would highlight code errors in an IDE.
Uber Chief Scientist Zoubin Ghahramani explains how artificial intelligence went from academia to real-world applications, and how Uber uses it to make transportation better.
Engineering manager Tory Schober talks about creating an Uber Freight engineering team in Chicago, and how that team will benefit from being close to its customer base.
At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.
Uber AI Labs proposes Loss Change Allocation (LCA), a new method that provides a rich window into the neural network training process.
We built Cyborg, an open source implementation of VectorDrawable for iOS, to more easily implement designs across our apps.
Uber ATG Web Platform intern Anat Kleiman shares her advice for testing React version 16 components when altering application logic.
Uber Engineering details how GraphQL integrated into our Customer Care platform, making for more targeted queries and reducing server load.
Waleed Kadous, Principal Engineer at Uber, enjoys tackling technical challenges that bridge the digital and physical duality of Uber's problem space.