Engineering

The technology behind Uber Engineering

Engineering

Tricks of the Trade: Tuning JVM Memory for Large-scale Services

February 26, 2020 / Global

Culture, Engineering

Building the Future of Mobility from the Pacific Northwest: Meet the Uber Seattle Tech Team

February 18, 2020 / Global

Uber AI, Data / ML

Building a Backtesting Service to Measure Model Performance at Uber-scale

February 13, 2020 / Global

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Stay up to date with the latest from Uber Engineering

Engineering, Web

Counting Calories: How We Improved the Performance and Developer Experience of UberEats.com

February 12, 2020 / Global

Culture, Data / ML

Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond

January 28, 2020 / Global

Data / ML

Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing

January 23, 2020 / Global

Data / ML, Engineering

Engineering SQL Support on Apache Pinot at Uber

January 15, 2020 / Global

Engineering

Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning

January 7, 2020 / Global

Engineering, Data / ML, Web

Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp

January 2, 2020 / Global

Engineering

Uber Open Source in 2019: Community Engagement and Contributions

December 20, 2019 / Global

Engineering

Year in Review: 2019 Highlights from the Uber Engineering Blog

December 19, 2019 / Global

Engineering

Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction

December 19, 2019 / Global

Uber AI

Uber AI in 2019: Advancing Mobility with Artificial Intelligence

December 18, 2019 / Global

Uber AI

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

December 18, 2019 / Global

Data / ML, Engineering

Uber’s Data Platform in 2019: Transforming Information to Intelligence

December 17, 2019 / Global

Data / ML, Engineering

Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

December 10, 2019 / Global

Engineering, Uber AI

Controlling Text Generation with Plug and Play Language Models

December 5, 2019 / Global

Page 16

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English ελληνικά

Engineering

Tricks of the Trade: Tuning JVM Memory for Large-scale Services

Building the Future of Mobility from the Pacific Northwest: Meet the Uber Seattle Tech Team

Building a Backtesting Service to Measure Model Performance at Uber-scale

Interested in joining Uber Eng?

Stay up to date with the latest from Uber Engineering

Counting Calories: How We Improved the Performance and Developer Experience of UberEats.com

Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond

Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing

Engineering SQL Support on Apache Pinot at Uber

Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning

Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp

Uber Open Source in 2019: Community Engagement and Contributions

Year in Review: 2019 Highlights from the Uber Engineering Blog

Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction

Uber AI in 2019: Advancing Mobility with Artificial Intelligence

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

Uber’s Data Platform in 2019: Transforming Information to Intelligence

Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

Controlling Text Generation with Plug and Play Language Models

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