We implemented a Kappa architecture at Uber to effectively backfill streaming data at scale, ensuring accurate data in our platform.
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.
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 recounts its many engagements with the open source community during 2019, from contributing projects to joining and founding new open source support organizations.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Developed by Uber AI Labs, Generative Teaching Networks (GTNs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
Plug and Play Language Model, introduced by Uber AI Labs, gives NLP practitioners the flexibility to plug in one or more simple attribute models into a large, unconditional language model.
Uber is presenting 11 papers at the NeurIPS 2019 conference in Vancouver, Canada, as well as sponsoring workshops including Women in Machine Learning (WiML) and Black in AI.
Uber introduces RxCentral, an open source library to reliably and repeatedly connect Bluetooth devices using a platform-agnostic, reactive design.
Uber's 2020 AI Residency will focus on initiatives related to our self-driving car project through Uber Advanced Technology Group (ATG).
Uber engineers describe Cadence, Uber’s open source workflow orchestration tool, its architecture, and its use in a series of informative presentations.
Uber built beacon to improve vehicle location accuracy on our platform, leading to more seamless rider pickup and dropoff experiences.
With the release of deck.gl version 7.3, Uber’s open source visualization tool now supports rendering massive geospatial data sets formatted according to the OGC 3D Tiles community standard.
Uber's IT Engineering team builds the tools and systems that help other Uber employees do their jobs. Meet a few of these remarkable behind-the-scenes engineers.
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
Uber has embraced Presto, a high performance, distributed SQL query engine, and joined the Presto Foundation. Meet the Uber engineers who contribute to and use Presto on a daily basis.
When developing Uber's self driving car systems, engineers found a way to identify edge case scenarios amongst terabytes of sensor data representing real-world situations.
CTO Thuan Pham sat down with former intern, now employee, Sudhanshu Mishra to talk about his early experiences in the technology industry and growing Uber.
Uber introduces Hypothesis GU Func, a new extension to Hypothesis, as an open source Python package for unit testing.