Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
Uber Engineering explains the technical reasoning behind its switch in database technologies, from Postgres to MySQL.
AresDB, Uber's open source real-time analytics engine, leverages GPUs to enable real-time computation and data processing in parallel.
Uber developed H3, our open source grid system for optimizing ride pricing and dispatch, to make geospatial data visualization and exploration easier and more efficient.
Base Web is a React component library which implements the Base design language to act as a device-agnostic foundation for easily creating web applications.
Developed by Uber, Kraken is an open source peer-to-peer Docker registry capable of distributing terabytes of data in seconds.
Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
Uber announces the release of the Autonomous Visualization System (AVS) as an open source project. AVS is a standard for creating a visual environment based on sensor data from autonomous vehicles, with playback available in multiple formats, including the web and video.
From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox
Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.
Noticing increased latency in our metrics platform, Uber engineers track down a bug related to stack growth in a goroutine, resulting in a fix elevated to the Go open source GitHub repository.
First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.
Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
To determine if it made sense to adopt Kotlin for our Android monorepo, Uber's Mobile Engineering team measured Kotlin build performance at scale across a variety of project structures.
Written in Haskell, Queryparser is Uber Engineering's open source tool for parsing and analyzing SQL queries that makes it easy to identify foreign-key relationships in large data warehouses.
This article is about developing Uber Engineering's open source distributed tracing system, Jaeger.
Uber Engineering's data processing platform team recently built and open sourced Hudi, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hudi powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.
Fusion.js, Uber's new open source web framework, supports modern features and integrations that make it easy to build lightweight, high-performing apps for the web.
Uber open sourced JVM Profiler, our distributed profiler, to enable others to seamlessly collect JVM performance and resource usage metrics.
The end of a two-part series on the tech stack that Uber Engineering uses to make transportation as reliable as running water, everywhere, for everyone, as of spring 2016.