One-click chat, the Uber driver app's smart reply system, leverages machine learning to make in-app messaging between driver-partners and riders more seamless.
Uber's Advanced Technologies Group introduces Petastorm, an open source data access library enabling training and evaluation of deep learning models directly from multi-terabyte datasets in Apache Parquet format.
Joe Zhou, the 7th iOS engineer on the Uber Eats team, offers advice for those considering taking the plunge into programming.
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop
Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
In an interview for the Uber Eng blog, Data Scientist Sunny Jeon talks about how his team develops solutions in order to advance Uber's core value of safety.
Uber Eats engineers describe how they surface restaurant recommendations in the app using multi-objective optimization to give eaters the most satisfying experience while maintaining the health of the Uber Eats marketplace.
Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.
Afshine and Shervine Amidi, identical twins, discuss their journeys to data science and how their work at Uber helps teams improve user experiences on our platform.
Uber developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.
A few of Uber's over 200 engineering interns from this year's summer program talk about the projects they worked on and what their experiences in the office were like.
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
Fusion.js, Uber's new open source web framework, supports modern features and integrations that make it easy to build lightweight, high-performing apps for the web.
Facing the need for a resilient data structure over thousands of storage nodes to serve the 15 million rides per day that occur on our platform, Uber engineers developed Herb, our data replication solution. Herb ensures data availability and integrity across our data centers.
Building robust regression analysis not only helps ensure that new feature deployments work properly, but also lets Uber engineers automate deployments.
Maps make up the bedrock of Uber's transportation solutions. Find out how we ensure the quality of our map data through extensive metrics computation, maintaining fidelity to real world locations and pinpointing allowable pick up and drop off locations for riders.
As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.