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Introducing Ludwig, a Code-Free Deep Learning Toolbox

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.

Why Financial Planning is Exciting… At Least for a Data Scientist

Model flow showing rider and driver sign-ups
In this article, Uber’s Marianne Borzic Ducournau discusses why financial planning at Uber presents unique and challenging opportunities for data scientists.

Building Locally, Scaling Globally: Meet the Tech Team at Uber New York City

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.

Increasing Representation at Uber through The Hidden Genius Project

Collage showing members and mentors for the Career Prep Program
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.

Introducing AresDB: Uber’s GPU-Powered Open Source, Real-time Analytics Engine

AresDB, Uber's open source real-time analytics engine, leverages GPUs to enable real-time computation and data processing in parallel.

How Uber Leverages Applied Behavioral Science at Scale

Uber Labs utilizes insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for users on our platform.

Expanding Access: Engineering Uber Lite

Uber Lite pickup screens
Many people around the world use Android phones based on hardware developed in 2015 and earlier. Uber engineers explain how they developed a lightweight rider app to serve this global audience.

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.

Uber Joins the Linux Foundation’s OpenChain Project as a Platinum Member

As part of the OpenChain Project’s governing board, Uber will help create best practices and define standards for open source software compliance.

Engineering Uber’s Next-Gen Payments Platform

During a September 2018 meetup, Uber's Payments Platform team discusses how this technology supports our company's growth through an active-active architecture, exactly-once payment processing, and scalability across businesses.

Sessionizing Uber Trips in Real Time

Image of birds flying
Uber's many data flows required modeling the data associated with a specific task, such as a rider trip, into a state machine. The state machine lets engineers focus on just the events needed to successfully accomplish a trip.

How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN

Metropolis-Hastings Generative Adversarial Networks (GANs) leverage the discriminator to pick better samples from the generator after ML model training is done.

How Uber’s New Driver App Overcomes Network Lag

Carbon: Optimistic Mode article feature image
In our continuing series about building our new driver app, Uber engineers discuss building its Optimistic Mode feature, which lets the app continue functioning while traversing network lag areas.

Montezuma’s Revenge Solved by Go-Explore, a New Algorithm for Hard-Exploration Problems (Sets Records on Pitfall, Too)

Uber AI Labs introduces Go-Explore, a new reinforcement learning algorithm for solving a variety of challenging problems, especially in robotics.

Collaboration at Scale: Highlights from Uber Open Summit 2018

Uber AI Chief Scientist Zoubin Ghahramani speaks at Uber Open Summit 2018
Uber hosted its first Open Summit on November 15, inviting the open source community to learn about our open source projects from the engineers who use them every day. Check out highlights from the day, including keynotes from the Linux Foundation's Jim Zemlin and Uber AI's Zoubin Ghahramani.

Observability at Scale: Building Uber’s Alerting Ecosystem

Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.

Uber Joins the Linux Foundation as a Gold Member

Announced during the Uber Open Summit 2018, we extend our commitment to open source by joining the Linux Foundation as a Gold Member.

Experience in AI: Uber Hires Jan Pedersen

Jan Pedersen announcement feature image
Uber welcomes Jan Pedersen as a Distinguished Scientist to our Uber AI group, where he will bring his extensive experience to our efforts in improving artificial intelligence and machine learning.

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.

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