Uber Poet, an open source mock application generator, helped us determine if refactoring the application part of our code into a few large modules would make our overall Swift build times faster.
We redesigned Uber's web-based booking flow for riders who prefer a browser over the app, simplifying pickup options and speeding up interactivity.
During our 2019 Uber European Technology Showcase, technical teams across the company discussed how we build products that drive safe and reliable transportation.
Ludwig version 0.2 integrates with Comet.ml, adds a new serving functionality, and incorporates the BERT text encoder, among other new features.
Uber Director of Data Science Franziska Bell discusses how we created data science platforms at Uber, letting employees of all technical skills perform forecasts and analyze data.
Uber AI Labs releases EvoGrad, a library for catalyzing gradient-based evolution research, and Evolvability ES, a new meta-learning algorithm enabled by this library.
The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.
Uber's MoneyCon brought together industry leaders to discuss the latest technologies and key learnings in the payments and finance engineering space.
In a selection of presentations delivered at a June 2019 Uber meetup, we discuss how to use H3, our open source hexagonal indexing system, to facilitate the granular mining of large geospatial data sets.
As head of Uber's Advanced Technologies Center in Paris, Francois Sillion and his team are responsible for supporting the R&D behind Uber Air, our effort to add a third dimension to our platform using flying vehicles.
Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.
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.