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Tag: Uber Engineering

Introducing Base Web, Uber’s New Design System for Building Websites in React

Base Web is a React component library which implements the Base design language to act as a device-agnostic foundation for easily creating web applications.

Uber Open Source: Catching Up with Celina Ward, M3 Observability Engineer

Celina Ward discusses her journey to engineering, what it was like to present at Kubecon 2018, and what’s next for M3, Uber's open source metrics platform.

Scaling Mobile Device Management for macOS with Chef at Uber

Uber's IT Engineering team scaled mobile device management on macOS by leveraging open source tools and custom API-driven Chef cookbooks.

Open Sourcing Peloton, Uber’s Unified Resource Scheduler

First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.

Using Machine Learning to Ensure the Capacity Safety of Individual Microservices

Uber leveraged machine learning to design our capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.

Introducing Kraken, an Open Source Peer-to-Peer Docker Registry

Developed by Uber, Kraken is an open source peer-to-peer Docker registry capable of distributing terabytes of data in seconds.
Subway platform

Architecting a Safe, Scalable, and Server-Driven Platform for Driver Preferences with RIBs

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.

Creating Custom Plugins with Fusion.js, Uber’s Open Source Web Framework

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 PySpark and Apache MXNet and Additional Features for Faster Training

Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.

Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro...

We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.

Meet Uber Sofia’s Women in Tech

Uber Sofia is also home to a vibrant community of women technologists, from software engineers and data analysts to designers and program managers.

Modeling Censored Time-to-Event Data Using Pyro, an Open Source Probabilistic Programming Language

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.

First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL

The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.

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.

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.

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.

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...

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

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.
Uber Freight truck driving down freeway

Profiles in Coding: Sylvain Francois, Uber Freight

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 Uber Open Source

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

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

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.

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.

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.

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.

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.

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.
Photo of Uber app showing map

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

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

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.
Food Discovery with Uber Eats: Recommending for the Marketplace

Food Discovery with Uber Eats: Recommending for the Marketplace

Uber Eats engineers describe how they surface restaurant recommendations in the app using multi-objective optimization to give eaters the most satisfying experience while maintaining the health of the Uber Eats marketplace.

Under the Hood of Uber’s Experimentation Platform

Uber's experimentation platform empowers us to improve the customer experience by allowing teams to launch, debug, measure, and monitor product changes.

Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning

Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.

Seeing Double: Meet Uber’s Identical Twin Data Scientists

Afshine and Shervine Amidi, identical twins, discuss their journeys to data science and how their work at Uber helps teams improve user experiences on our platform.

Maximizing Process Performance with Maze, Uber’s Funnel Visualization Platform

Uber developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.

Databook: Turning Big Data into Knowledge with Metadata at Uber

Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.

Introducing Fusion.js: A Plugin-based Universal Web Framework

Fusion.js, Uber's new open source web framework, supports modern features and integrations that make it easy to build lightweight, high-performing apps for the web.

H3: Uber’s Hexagonal Hierarchical Spatial Index

Uber developed H3, our open source grid system for optimizing ride pricing and dispatch, to make geospatial data visualization and exploration easier and more efficient.

M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model

With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.

Ten Questions with Angus Croll, Author of If Hemingway Wrote JavaScript

Inspired by his love of reading, Uber engineer Angus Croll wrote a book dedicated to mimicking the literary styles of famous authors—in JavaScript.

Going Global: Highlights from the Second Annual Uber Technology Day

On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.

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