Tag: Uber Engineering
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
We built Cyborg, an open source implementation of VectorDrawable for iOS, to more easily implement designs across our apps.
With zero downtime, Uber's Payments Engineering team embarked on a migration that would allow authorization hold logic to be written once and used across existing and future payments products.
Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.
Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.
Jennifer Anderson, a veteran of Silicon Valley technology companies, leads Uber's Product Platform organization, which hosts our core services. In this interview, she describes her organization and the lessons she has learned.
On May 3, 2019, Uber’s Programming Systems Team hosted the Programming Systems and Tools Track of the company’s Second Uber Science Symposium, featuring a full day of talks by leading researchers and practitioners in the the field.
In this article, we share highlights from Uber’s annual Take Your Kids to Work Day celebration, an opportunity for the children of Uber parents to get a taste of what it’s like to work at a technology company.
Base Web is a React component library which implements the Base design language to act as a device-agnostic foundation for easily creating web applications.
Celina Ward discusses her journey to engineering, what it was like to present at Kubecon 2018, and what’s next for M3, Uber's open source metrics platform.
Uber's IT Engineering team scaled mobile device management on macOS by leveraging open source tools and custom API-driven Chef cookbooks.
First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.
Uber leveraged machine learning to design our capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.
Developed by Uber, Kraken is an open source peer-to-peer Docker registry capable of distributing terabytes of data in seconds.
Our driver app's new server-driven preferences section enables driver-partners to customize their experiences to make the app better fit into their lives.
In addition to providing official plugins, Fusion.js enables developers to build and integrate their own plugins by leveraging dependency injection.
Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.