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Containerizing Apache Hadoop Infrastructure at Uber

Introduction As Uber’s business grew, we scaled our Apache Hadoop (referred to as ‘Hadoop’ in this article) deployment to 21000+ hosts in 5 years, to...

‘Orders Near You’ and User-Facing Analytics on Real-Time Geospatial Data

Introduction By its nature, Uber’s business is highly real-time and contingent upon geospatial data. PBs of data are continuously being collected from our drivers, riders,...

Analyzing Customer Issues to Improve User Experience

Introduction The primary goal for customer support is to ensure users’ issues are addressed and resolved in a timely and effective manner. The kind of...

Customer Support Automation Platform at Uber

High Level Overview of the Problem Introduction If you’ve used any online/digital service, chances are that you are familiar with what a typical customer service experience...

Tuning Model Performance

Introduction Uber uses machine learning (ML) models to power critical business decisions. An ML model goes through many experiment iterations before making it to production....

Elastic Distributed Training with XGBoost on Ray

Introduction Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases...

Continuous Integration and Deployment for Machine Learning Online Serving and Models

Introduction At Uber, we have witnessed a significant increase in machine learning adoption across various organizations and use-cases over the last few years. Our machine...

Introducing Orbit, An Open Source Package for Time Series Inference and Forecasting

Orbit is a general interface for Bayesian time series modeling. The goal of Orbit development team is to create a tool that is easy...

Optimal Feature Discovery: Better, Leaner Machine Learning Models Through Information Theory

Introduction  Suppose you own a production ML model that already works reasonably well. You know that adding relevant and diverse sources of signal to your...

Freight Pricing with a Controlled Markov Decision Process

Intro Uber Freight was launched in 2017 to revolutionize the business of matching shippers and carriers in the huge and inefficient freight trucking industry (around...

Elastic Deep Learning with Horovod on Ray

Introduction In 2017, we introduced Horovod, an open source framework for scaling deep learning training across hundreds of GPUs in parallel.  At the time, most...

Horovod v0.21: Optimizing Network Utilization with Local Gradient Aggregation and Grouped Allreduce

We originally open-sourced Horovod in 2017, and since then it has grown to become the standard solution in industry for scaling deep learning training...

Revolutionizing Money Movements at Scale with Strong Data Consistency

Uber as a platform invites its users to leverage it, earn from it, and be delighted by it. Serving more than 18 million requests...

Fiber: Distributed Computing for AI Made Simple

Project Homepage: GitHub Over the past several years, increasing processing power of computing machines has led to an increase in machine learning advances. More and...

Uber Visualization Highlights: How Urban Symphony Adds an Audio Dimension to Visualization

As part of Uber Visualization's all-team hackathon, we built Urban Symphony, an Uber Movement visualization that adds an audio component to traffic speed patterns.

Enabling Collaboration through Open Source: Highlights from Uber Open Summit Sofia 2019

At the Uber Open Summit Sofia 2019, we showcased how open source technologies are driving the future of artificial intelligence, site reliability, and other domains.

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.

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.

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