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.
Rick Boone, Strategic Advisor for Uber's Core Infrastructure group, talks about his journey from his work in site reliability to his current role in long-term planning for infrastructure health and scalability.
Uber engineers share their learnings on how to tune a Java Virtual Machine so as to avoid long pauses and other issues with garbage collection.
The Uber Seattle Tech team is responsible for building a diverse range of technologies, from developer tools to our data platform architecture.
We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.
Improving the performance and developer velocity for the Uber Eats web application involved a complete rewrite, developing a new architecture and using Fusion.js.
In October 2019, Uber hosted our second annual Moving The World With Data meetup, showcasing some of our most interesting data science challenges in 2019.
We implemented a Kappa architecture at Uber to effectively backfill streaming data at scale, ensuring accurate data in our platform.