NVIDIA: Accelerating Deep Learning with Uber’s Horovod

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.

Announcing the 2019 Uber AI Residency

The Uber AI Residency is a 12-month training program for academics and professionals interested in becoming an AI researcher with Uber AI Labs or Uber ATG.

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.

Architecting Uber’s New Driver App in RIBs

Architecting Uber's New Driver App in RIBs feature image
In our continuing series about building our new driver app, Uber engineers discuss designing the architecture of the mobile app using RIBs, our open source mobile development framework.

Analyzing Experiment Outcomes: Beyond Average Treatment Effects

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.

Scaling Machine Learning at Uber with Michelangelo

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.

Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.

Transforming Payments & Empowering Developers: Meet the Uber Amsterdam Tech Team

Home to Uber's Payments and Developer Platform teams, Uber Amsterdam is the company's largest engineering office outside of the U.S.

Preview 7 Open Source Projects from the Uber Open Summit

Uber open source logo
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.

Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development

Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.

Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps

Photo of Uber app showing map
To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.

Open Source at Uber: A Conversation with Yuri Shkuro, Jaeger Project Lead

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.

Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

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 Expands Advanced Visualization Ecosystem with Mapbox Integration

Uber Visualization announces partnership with Mapbox to enhance our data visualization tools and grow our open source community.

Why We Decided to Rewrite Uber’s Driver App

Why We Decided to Rewrite Uber's Driver App
Uber engineers outline how we came to the resource-intensive decision to rewrite, rather than migrate or update, our driver app.

Seven Things to Know about Technical Writing at Uber

Technical writer and former intern Shannon Brown explains her work and answers common questions about this important role in Uber’s engineering organization.

Announcing Uber Open Summit 2018: Collaboration at Scale

Keynote speakers include Jim Zemlin, executive director of the Linux Foundation, and Zoubin Ghahramani, chief scientist at Uber AI Labs.

Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System

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.

Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning

Petastorm logo
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.

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