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The Journey Towards Metric Standardization

At Uber, business metrics are vital for discovering insights about how we perform, gauging the impact of new products, and optimizing the decision making...

Editing Massive Geospatial Data Sets with nebula.gl

Uber built and open sourced nebula.gl, a tool set for full-featured geospatial editing in the web browser, to better visualize large-scale data sets.

Building a Large-scale Transactional Data Lake at Uber Using Apache Hudi

The Apache Hudi team at Uber reflects on the open source project's history as it graduates to a Top Level Project under the Apache Software Foundation.

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.

Profiles in Coding: Christabelle Bosson, Uber Elevate, San Francisco

Christabelle Bosson, a senior advanced airspaces services engineer, discusses her journey from NASA to Uber Elevate, what excites her about the future of aerial ridesharing, and advice for aspiring aerospace engineers.

Developing the Next Generation of Coders with the Dev/Mission Uber Coding Fellowship

Participants in the Dev/Mission <> Uber Coding Fellowship took weekly courses taught by Uber engineers and worked with volunteers from Code for San Francisco on projects that benefit the local community.
Go and Bazel logos

Building Uber’s Go Monorepo with Bazel

When Uber adopted the open source Bazel build system, our engineers found many opportunities to contribute improvements to how Bazel works with a large Go monorepo.
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Monitoring Data Quality at Scale with Statistical Modeling

Uber employs statistical modeling to find anomalies in data and continually monitor data quality.

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Building upon our existing open-ended learning research, Uber AI released Enhanced POET, a project that incorporates an improved algorithm and allows for more diverse training environments.

Introducing Piranha: An Open Source Tool to Automatically Delete Stale Code

Uber developed Piranha to seamlessly delete code related to obsolete feature flags, leading to improved developer productivity and a cleaner codebase.

Fostering a Culture of Sponsorship: Introducing Uber’s Engineering and Sponsorship Development Program

Designed by Uber's Office of the CTO, the Engineering Sponsorship and Development Program (ESDP) pairs participants with sponsors and provides an opportunity to hone technical leadership skills.
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Why We Leverage Multi-tenancy in Uber’s Microservice Architecture

Multi-tenancy lets Uber tag requests coming into our microservice architecture, giving us the flexibility to route requests to specific components, such as during testing scenarios.

Celebrating International Women’s Day: Meet the Women Building Uber’s Global Platform

To celebrate International Women's Day, we spoke with women from across the company whose work helps deliver impactful experiences for Uber users worldwide.
Uber open source logo

Introducing Uber’s Open Source Principles

Uber shares our principles and goals for using and contributing open source software, providing visibility into our company's approach to open source.
Uber ATG self-driving cars

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.
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Profiles in Coding: Rick Boone, Core Infrastructure, San Francisco

Rick Boone, Strategic Advisor for Uber's Core Infrastructure group, talks about his journey from his work in site reliability to his current role in long-term planning for infrastructure health and scalability.
latency graph

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

Uber engineers share their learnings on how to tune a Java Virtual Machine so as to avoid long pauses and other issues with garbage collection.

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.
Screenshots from UberEats.com

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

Improving the performance and developer velocity for the Uber Eats web application involved a complete rewrite, developing a new architecture and using Fusion.js.

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.

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