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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

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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

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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

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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.

From Financial Models to iOS Model View Controllers: Making a Career Move to Programming

Joe Zhou, the 7th iOS engineer on the Uber Eats team, offers advice for those considering taking the plunge into programming.

Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop

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Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.

Talking Safety with Uber Data Scientist Sunny Jeon

Sunny Jeon
In an interview for the Uber Eng blog, Data Scientist Sunny Jeon talks about how his team develops solutions in order to advance Uber's core value of safety.

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.

Forecasting at Uber: An Introduction

In this article, we provide a general overview of how our teams leverage forecasting to build better products and maintain the health of the Uber 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.

Out of the Classroom: A Snapshot of Uber’s Summer 2018 Interns

Summer 2018 Uber Eng Interns
A few of Uber's over 200 engineering interns from this year's summer program talk about the projects they worked on and what their experiences in the office were like.

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