Project Homepage: GitHub Over the past several years, increasing processing power of computing machines has led to an increase in machine learning advances. More and more, algorithms exploit parallelism and rely on distributed training to process an enormous amount of data. However, the resulting need to increase both data and training impose great challenges on the software that manages and...
Uber built and open sourced nebula.gl, a tool set for full-featured geospatial editing in the web browser, to better visualize large-scale data sets.
Self-driving cars have long been considered the future of transportation, but they’re becoming more present everyday. Uber ATG (Advanced Technologies Group) is at the forefront of this technology, helping bring safe, reliable self-driving vehicles to the streets. Of course, this wouldn’t be possible without the work of the engineers building the ATG platform’s underlying technologies. As the engineering manager for...
The Apache Hudi team at Uber reflects on the open source project's history as it graduates to a Top Level Project under the Apache Software Foundation.
Developed by Uber ATG, Neuropod is an abstraction layer that provides a universal interface to run models across any deep learning framework.
Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning
Uber ATG's self-driving vehicles measure a multitude of possible scenario variations to answer the age-old question: "how does the pedestrian cross the road?"
Uber AI introduces Meta-Graph, a new few-shot link prediction framework that facilitates the more accurate training of ML models that quickly adapt to new graph data.
Christabelle Bosson, a senior advanced airspaces services engineer, discusses her journey from NASA to Uber Elevate, what excites her about the future of aerial ridesharing, and advice for aspiring aerospace engineers.
Participants in the Dev/Mission <> Uber Coding Fellowship took weekly courses taught by Uber engineers and worked with volunteers from Code for San Francisco on projects that benefit the local community.
Uber ATG built Athenadriver, an open source Amazon Athena database driver for Go, to facilitate communication between our business intelligence tools and the cloud.
Uber AI released a new framework on top of Pyro that lets experimenters seamlessly automate optimal experimental design (OED) for quicker model iteration.
Uber employs statistical modeling to find anomalies in data and continually monitor data quality.
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Building upon our existing open-ended learning research, Uber AI released Enhanced POET, a project that incorporates an improved algorithm and allows for more diverse training environments.
Uber developed Piranha to seamlessly delete code related to obsolete feature flags, leading to improved developer productivity and a cleaner codebase.
Fostering a Culture of Sponsorship: Introducing Uber’s Engineering and Sponsorship Development Program
Designed by Uber's Office of the CTO, the Engineering Sponsorship and Development Program (ESDP) pairs participants with sponsors and provides an opportunity to hone technical leadership skills.
Multi-tenancy lets Uber tag requests coming into our microservice architecture, giving us the flexibility to route requests to specific components, such as during testing scenarios.
To celebrate International Women's Day, we spoke with women from across the company whose work helps deliver impactful experiences for Uber users worldwide.
Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles
Managing multiple machine learning models to enable self-driving vehicles is a challenge. Uber ATG developed a model life cycle for quick iterations and a tool for continuous delivery and dependency management.