Overview Data access restrictions, retention, and encryption at rest are fundamental security controls. This blog explains how we have built and utilized open-sourced Apache Parquet™'s...
Introduction Cadence is a multi-tenant orchestration framework that helps developers at Uber to write fault-tolerant, long-running applications, also known as workflows. It scales horizontally to...
Introduction In 2017, we introduced Horovod, an open source framework for scaling deep learning training across hundreds of GPUs in parallel. At the time, most...
We originally open-sourced Horovod in 2017, and since then it has grown to become the standard solution in industry for scaling deep learning training...
Introduction Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence...
In February 2019, Uber released Ludwig, an open source, code-free deep learning (DL) toolbox that gives non-programmers and advanced machine learning (ML) practitioners alike...
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.
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.
Developed by Uber ATG, Neuropod is an abstraction layer that provides a universal interface to run models across any deep learning framework.
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.
Uber ATG built Athenadriver, an open source Amazon Athena database driver for Go, to facilitate communication between our business intelligence tools and the cloud.
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.
Uber AI released a new framework on top of Pyro that lets experimenters seamlessly automate optimal experimental design (OED) for quicker model iteration.
Uber developed Piranha to seamlessly delete code related to obsolete feature flags, leading to improved developer productivity and a cleaner codebase.
Uber shares our principles and goals for using and contributing open source software, providing visibility into our company's approach to open source.
We engineered full SQL support on Apache Pinot to enable quick analysis and reporting on aggregated data, leading to improved experiences on our platform.
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 recounts its many engagements with the open source community during 2019, from contributing projects to joining and founding new open source support organizations.
Uber introduces RxCentral, an open source library to reliably and repeatedly connect Bluetooth devices using a platform-agnostic, reactive design.
Uber’s observability engineers present their work on distributed tracing (Jaeger), sampling (XYS), and metrics processing (M3).