Aarhus Engineering Internship: Building Aggregation Support for YQL, Uber’s Graph Query Language for Grail

Uber intern Lau Skorstengaard shares his experience working on YQL, the graph query language for our in-house infrastructure state aggregation platform.

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.

Building a Scalable and Reliable Map Interface for Drivers

Uber driver app screen
In our ongoing series about rewriting the Uber driver app, engineer Chris Haugli explains how we designed the map display to be resilient, and always show the most useful information.

Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.

POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer

Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.

From Self-Driving Cars to Optimizing Claims Efficiency: My Unconventional Journey to Insurance Engineering

In this article, engineering manager Lili Kan reflects on her decision to lead Uber's Insurance Engineering team and discusses the challenges—and opportunities—of building insurance products for our platform.

How to Ship an App Rewrite Without Risking Your Entire Business

How to Ship an App Rewrite Without Risking Your Entire Business
Rather than shipping out our new driver app as a simple update to Android phones, Uber engineers delivered a dual binary package, enabling a safe and structured rollout of the new app while maintaining support for the previous version.

Women in Data Science at Uber: Moving the World With Data

During an October 2018 meetup, members of our Women in Statistics, Data, Optimization, and Machine Learning (WiSDOM) group presented on their technical work at Uber.

Profiles in Coding: Sylvain Francois, Uber Freight

Uber Freight truck driving down freeway
For Uber's Profiles in Coding series, we interview Uber Freight engineer Sylvain Francois to find out the nature of his daily work and his best tips for coders.

How Uber Beacon Helps Improve Safety for Riders and Drivers

The Uber Beacon leverages visual signaling, an accelerometer, and a gyroscope to improve the accuracy of in-app safety products like our automatic crash detection feature.

Year in Review: 2018 Highlights from the Uber Engineering Blog

Our editors spotlight some of the year's most popular articles, from an overview of our Big Data platform to a first-person account of an engineer's immigrant journey.

Year in Review: 2018 Highlights from Uber Open Source

Brian Hsieh, Uber's Open Source program lead, reflects on open source accomplishments, project launches, and collaborations in 2018.

Four Ways Uber Visualization Made an Impact in 2018

Uber's Head of Urban Computing & Visualization reflects on his team's work visualizing data to better understand urban mobility in 2018—and beyond.

Interning at Uber: Building the Uber Eats Menu Scheduler

Jonathan Levi recounts his experience as an intern at Uber during Summer 2018, including building a useful project for the Uber Eats team.

Horovod Joins the LF Deep Learning Foundation as its Newest Project

Horovod, Uber's distributed training framework, joins the LF Deep Learning Foundation to help advance open source innovation in AI, ML, and deep learning.

Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead

We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.

Scaling Cash Payments in Uber Eats

Scaling Cash Payments in Uber Eats - feature_image
Uber's new driver app leverages its offline mode along with a cash-drop system organized around restaurants so that Uber Eats customers can pay for deliveries with cash.

Faster Neural Networks Straight from JPEG

Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations.

The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series Data

Part of Uber's open source M3 metrics system, our query engine can support real-time, large-scale computation and multiple query languages.

Introducing Makisu: Uber’s Fast, Reliable Docker Image Builder for Apache Mesos and Kubernetes

Uber built Makisu, our open source Docker image builder, to enable the quick, reliable generation of Dockerfiles in Mesos and Kubernetes ecosystems.

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