Uber's Maps Collection and Reporting (MapCARs) team shares best practices when choosing which HDFS file formats are optimal for use with Apache Spark.
How engineers and data scientists at Uber came together to come up with a means of partially replicating Vertica clusters to better scale our data volume.
Uber engineers offer two common use cases showing how we orchestrate machine learning model training in our data workflow engine.
DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data Lake
Uber engineers discuss the development of DBEvents, a change data capture system designed for high data quality and freshness that is capable of operating on a global scale.
We spoke to Data Science Director Fran Bell about machine learning at Uber and what she finds most challenging—and rewarding—about her work.
Mitigating Risk in a Three-Sided Marketplace: A Conversation with Trupti Natu and Neel Mouleeswaran on the Uber Eats Risk Team
We sat down with a risk strategy manager and a risk engineer to discuss how they build solutions to minimize risk in the Uber Eats three-sided marketplace.
First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.
Uber leveraged machine learning to design our capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.
Developed by Uber, Kraken is an open source peer-to-peer Docker registry capable of distributing terabytes of data in seconds.
Engineering interns from Uber's European offices talk about their experiences, including the projects they worked on, the people they worked with, and the social activities they engaged in.
Our driver app's new server-driven preferences section enables driver-partners to customize their experiences to make the app better fit into their lives.
Uber site reliability engineer Tatiana Romanova, based in our Amsterdam engineering office, discusses her computer science background, her journey to Uber, and her work maintaining our Payments Platform.
In addition to providing official plugins, Fusion.js enables developers to build and integrate their own plugins by leveraging dependency injection.
Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.
Created by Uber in 2017, Pyro was voted in by the Linux Foundation Deep Learning Technical Board as the latest incubation project to join its foundation.
We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.
Uber announces the release of the Autonomous Visualization System (AVS) as an open source project. AVS is a standard for creating a visual environment based on sensor data from autonomous vehicles, with playback available in multiple formats, including the web and video.
Censored time-to-event data is critical to the proper modeling and understanding of customer engagement on the Uber platform. In this article, we demonstrate an easier way to model this data using Pyro.
In our ongoing series about rewriting the Uber driver app, engineer Kevin Babcock explains how we built the connection between the app and the Uber Beacon device, which displays a color remotely selected through a rider's app.
The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
In this article, Uber’s Marianne Borzic Ducournau discusses why financial planning at Uber presents unique and challenging opportunities for data scientists.
Ever wondered what it’s like to work in tech at Uber New York City? Just blocks from Times Square and Bryant Park, Uber’s new office in midtown Manhattan is home to more than a dozen teams, hundreds of employees (and growing), and a wide variety of engineering roles.
In its first year, the Career Prep Program, a collaboration between Uber and The Hidden Genius Project, demonstrated how technology-focused companies can embrace and reinforce values of diversity and inclusion among engineers, while having a positive impact in the community.
AresDB, Uber's open source real-time analytics engine, leverages GPUs to enable real-time computation and data processing in parallel.
Uber Labs utilizes insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for users on our platform.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Brian Hsieh, Uber's Open Source program lead, reflects on open source accomplishments, project launches, and collaborations 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.
Jonathan Levi recounts his experience as an intern at Uber during Summer 2018, including building a useful project for the Uber Eats team.
Horovod, Uber's distributed training framework, joins the LF Deep Learning Foundation to help advance open source innovation in AI, ML, and deep learning.
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.
Part of Uber's open source M3 metrics system, our query engine can support real-time, large-scale computation and multiple query languages.
Uber built Makisu, our open source Docker image builder, to enable the quick, reliable generation of Dockerfiles in Mesos and Kubernetes ecosystems.