Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.
My Journey from Working as a Fabric Weaver in Ethiopia to Becoming a Software Engineer at Uber in San Francisco
Samuel Zemedkun reflects on his immigrant experience and how his part-time driving through the Uber platform funded his education and inspired his decision to join the company.
Quantile treatment effects (QTEs) enable our data scientists to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the Uber marketplace.
Engineering Sustainability: An Interview with Uber’s Head of Information Technology, Shobhana Ahluwalia
We sat down with Uber's Head of Information Technology to discuss her journey to tech services, what she finds most challenging about her work at Uber, and how her team is setting the company up for success.
Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.
Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.
Home to Uber's Payments and Developer Platform teams, Uber Amsterdam is the company's largest engineering office outside of the U.S.
Uber open source projects leads give updates on seven of our projects, all of which will be showcased at the upcoming Uber Open Summit 2018.
Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.
To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.
Yuri Shkuro dicusses his journey to open source at Uber, his experience developing Jaeger, our open source distributed tracing system, and how to grow an open source community from scratch.
Responsible for cleaning, storing, and serving over 100 petabytes of analytical data, Uber's Hadoop platform ensures data reliability, scalability, and ease-of-use with minimal latency.
Uber Visualization announces partnership with Mapbox to enhance our data visualization tools and grow our open source community.
Technical writer and former intern Shannon Brown explains her work and answers common questions about this important role in Uber’s engineering organization.
Keynote speakers include Jim Zemlin, executive director of the Linux Foundation, and Zoubin Ghahramani, chief scientist at Uber AI Labs.