Uber Labs leverages causal inference, a statistical method for better understanding the cause of experiment results, to improve our products and operations analysis.
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.
Uber's Destination:Web meetup series gives great insight about the most current web building tools and techniques. These three videos from Uber presenters offer tips on a mysterious design pattern, the Elm language, and Progressive Enhancement.
A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In our development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe.
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.
Architecting the Uber driver app for Android, which needs to run for extended periods of time in the background, involved a unique idea where Activities and Services were not included in the structural foundations.
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.
On May 3, 2019, Uber’s Applied Behavioral Science team hosted the Behavioral Science Track of the Second Uber Science Symposium, featuring a full day of presentations delivered by leading researchers in the field.
Learn how to use Kepler.gl for data visualization through our tutorial, where we show how easy it is to load multiple datasets into Kepler.gl to visualize traffic safety in Manhattan.
Implementing QUIC protocol against TCP over cellular networks on our apps led to a reduction of 10-30 percent in tail-end latencies for HTTP traffic.
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.
In addition to joining the Urban Computing Foundation, Uber is contributing Kepler.gl, an open source geospatial analysis tool, as the organization's first hosted project.
Uber builds upon the Lottery Ticket Hypothesis by proposing explanations behind these mechanisms and deriving a surprising by-product: the Supermask.
Performing updates of individual records in Uber's over 100 petabyte Apache Hadoop data lake required building Global Index, a component that manages data bookkeeping and lookups at scale.
We submitted Hudi to the Apache Incubator to ensure the long-term growth and sustainability of the project under The Apache Software Foundation.
Noticing increased latency in our metrics platform, Uber engineers track down a bug related to stack growth in a goroutine, resulting in a fix elevated to the Go open source GitHub repository.