Tag: Machine Learning
Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine...
Uber ATG's self-driving vehicles measure a multitude of possible scenario variations to answer the age-old question: "how does the pedestrian cross the road?"
Meta-Graph: Few-Shot Link Prediction Using Meta-Learning
Uber AI introduces Meta-Graph, a new few-shot link prediction framework that facilitates the more accurate training of ML models that quickly adapt to new graph data.
Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for...
Managing multiple machine learning models to enable self-driving vehicles is a challenge. Uber ATG developed a model life cycle for quick iterations and a tool for continuous delivery and dependency management.
Building a Backtesting Service to Measure Model Performance at Uber-scale
We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.
Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond
In October 2019, Uber hosted our second annual Moving The World With Data meetup, showcasing some of our most interesting data science challenges in 2019.
Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning
First introduced by Uber Engineering in January 2019, Manifold is a visual debugging tool that enables users to quickly identify performance issues in machine learning models.
Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp
As part of Uber Visualization's all-team hackathon, we built SpeedsUp, a project using machine learning to process average speeds across a city, cluster the results, and overlay them on a street map.
Uber AI in 2019: Advancing Mobility with Artificial Intelligence
In 2019, Uber AI built tools and systems that leverage ML to improve location accuracy and enhance real-time forecasting, among other applications on our platform.
Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
Announcing the 2020 Uber AI Residency
Uber's 2020 AI Residency will focus on initiatives related to our self-driving car project through Uber Advanced Technology Group (ATG).
Evolving Michelangelo Model Representation for Flexibility at Scale
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
Science at Uber: Improving Transportation with Artificial Intelligence
Uber Chief Scientist Zoubin Ghahramani explains how artificial intelligence went from academia to real-world applications, and how Uber uses it to make transportation better.
Three Approaches to Scaling Machine Learning with Uber Seattle Engineering
At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
Science at Uber: Powering Machine Learning at Uber
Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.
Advancing AI: A Conversation with Jeff Clune, Senior Research Manager at Uber
We sat down with Jeff Clune, Senior Research Manager, to talk about his work in AI, journey to Uber, and Presidential Early Career Achievement in Science and Engineering (PECASE) award.
Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution
Uber AI Labs releases EvoGrad, a library for catalyzing gradient-based evolution research, and Evolvability ES, a new meta-learning algorithm enabled by this library.
Gaining Insights in a Simulated Marketplace with Machine Learning at Uber
Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.
No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox
Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.
Improving Uber’s Mapping Accuracy with CatchME
CatchMapError (CatchMe) is a system that automatically catches errors in Uber's map data with anonymized GPS traces from the driver app.
Accessible Machine Learning through Data Workflow Management
Uber engineers offer two common use cases showing how we orchestrate machine learning model training in our data workflow engine.
