Tag: Data

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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.

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.

Herb: Multi-DC Replication Engine for Uber’s Schemaless Datastore

Facing the need for a resilient data structure over thousands of storage nodes to serve the 15 million rides per day that occur on our platform, Uber engineers developed Herb, our data replication solution. Herb ensures data availability and integrity across our data centers.

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.

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.

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 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.

Detecting Abuse at Scale: Locality Sensitive Hashing at Uber Engineering

In this article, we discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.

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.

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