Skip to footer

Tag: Uber Data

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.

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.

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.
Image of birds flying

Sessionizing Uber Trips in Real Time

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.

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.

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.

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.

From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox

Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.

Growing the Data Visualization Community with deck.gl v5

deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.

Mediation Modeling at Uber: Understanding Why Product Changes Work (and Don’t Work)

Uber Labs leverages mediation modeling to better understand the relationship between product updates and their outcomes, leading to improved customer experiences on our platform.

Introducing the Uber AI Residency

Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.

Implementing Model-Agnosticism in Uber’s Real-Time Anomaly Detection Platform

Uber Engineering extended our anomaly detection platform's ability to integrate new forecast models, allowing this critical on-call service to scale to meet more complex use cases.

Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning

Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.

Welcoming Peter Dayan to Uber AI Labs

Arriving now: Uber's Chief Scientist Zoubin Ghahramani introduces Uber AI Labs' newest team member, award-winning neuroscientist Peter Dayan.

Engineering More Reliable Transportation with Machine Learning and AI at Uber

In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.

Turbocharging Analytics at Uber with our Data Science Workbench

Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.

Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow

Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.

Introducing AthenaX, Uber Engineering’s Open Source Streaming Analytics Platform

Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.

Engineering Restaurant Manager, our UberEATS Analytics Dashboard

The UberEATS Restaurant Manager gives restaurant partners insight into their business by measuring customer satisfaction, sales, and service quality.

Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

Uber Engineering introduces a new Bayesian neural network architecture that more accurately forecasts time series predictions and uncertainty estimations.

Meet Michelangelo: Uber’s Machine Learning Platform

Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.

Uber’s Ride with the Sun: Tracking the 2017 Solar Eclipse

Uber Engineering’s Data Visualization team uses their deck.gl and Voyager visualization platforms to map rider behavior during the August 21, 2017 solar eclipse.

Engineering Uber’s Self-Driving Car Visualization Platform for the Web

Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.

Engineering Uber Predictions in Real Time with ELK

Uber Engineering architected a real-time trip features prediction system using an open source RESTful search engine built with Elasticsearch, Logstash, and Kibana (ELK).

Engineering Data Analytics with Presto and Apache Parquet at Uber

Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.

Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.

Building an Intelligent Experimentation Platform with Uber Engineering

Composed of a staged rollout and intelligent analytics tool, Uber Engineering's experimentation platform is capable of stably deploying new features at scale across our apps. In this article, we discuss the challenges and opportunities we faced when building this product.

Redesigning Uber Engineering’s Mobile Content Delivery Ecosystem

How Uber Engineering re-architected the content delivery feed and backend ecosystem of our new driver app to deliver an enhanced user experience.

Presenting the Engineering Behind Uber at Our Technology Day

A daylong event at Uber’s Palo Alto office, sponsored by our LadyEng group, showcased the technical work across Uber Engineering as well as the people who are leading and building these projects. Here are some of the resulting presentations.

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop

Uber Engineering's data processing platform team recently built and open sourced Hudi, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hudi powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.

Visualize Data Sets on the Web with Uber Engineering’s deck.gl Framework

In this article, we discuss deck.gl, an open sourced, WebGL-powered framework specifically designed for exploring and visualizing data sets at scale.

The Uber Engineering Summer Intern Experience

As this summer comes to a close, profiles from last summer's Uber Engineering intern class and what their Uber experience was like.

Why Uber Engineering Switched from Postgres to MySQL

Uber Engineering explains the technical reasoning behind its switch in database technologies, from Postgres to MySQL.

The Uber Engineering Tech Stack, Part II: The Edge and Beyond

The end of a two-part series on the tech stack that Uber Engineering uses to make transportation as reliable as running water, everywhere, for everyone, as of spring 2016.

The Uber Engineering Tech Stack, Part I: The Foundation

Uber’s mission is transportation as reliable as running water, everywhere, for everyone. Here's the first of a two-part series on the tech stack that Uber Engineering uses to make this happen.

Engineering Uber Systems to Combat Fraud

Fraud prevention is one of Uber's fastest growing areas of research and development. As our platform has grown, so has the international underworld that tries to undermine it. Here’s how Uber engineers systems to fight fraud in 2016 and beyond.

How Uber Engineering Increases Safe Driving with Telematics

The engineering behind how Uber's Driving Safety team is using telematics to raise awareness of driving patterns to our partners.

Engineering Intelligence Through Data Visualization at Uber

The data visualization team in Uber Engineering delivers intelligence through crafting visual exploratory data analysis tools. Here's what some of these visualizations look like.

An Uber Engineer Discusses Cash for India Growth and Beyond

An Uber engineer discusses how introducing cash payments for rides has fostered Uber's growth in India and beyond.

Streamific, the Ingestion Service for Hadoop Big Data at Uber Engineering

Here we look at Hadoop data ingestion, and how Uber Engineering streams diverse data into a cohesive layer for querying in near real-time using our in-house developed Streamific.

Project Mezzanine: The Great Migration

What happens when you have to migrate hundreds of millions of rows of data and 100 services over several weeks with dozens of engineers, while simultaneously serving millions of rides? The story of how Uber moved to Mezzanine in 2014.

Popular Articles