Tag: Uber Engineering
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.
Uber AI released a new framework on top of Pyro that lets experimenters seamlessly automate optimal experimental design (OED) for quicker model iteration.
To celebrate International Women's Day, we spoke with women from across the company whose work helps deliver impactful experiences for Uber users worldwide.
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.
In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
As part of Uber Visualization's all-team hackathon, we built Urban Symphony, an Uber Movement visualization that adds an audio component to traffic speed patterns.
To simplify the Uber Eats experience for our restaurant-partners, we built Menu Maker, a web-based tool for seamlessly managing menus on the Uber Eats app.
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.
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
We built Cyborg, an open source implementation of VectorDrawable for iOS, to more easily implement designs across our apps.
With zero downtime, Uber's Payments Engineering team embarked on a migration that would allow authorization hold logic to be written once and used across existing and future payments products.
Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.
Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.
Jennifer Anderson, a veteran of Silicon Valley technology companies, leads Uber's Product Platform organization, which hosts our core services. In this interview, she describes her organization and the lessons she has learned.
On May 3, 2019, Uber’s Programming Systems Team hosted the Programming Systems and Tools Track of the company’s Second Uber Science Symposium, featuring a full day of talks by leading researchers and practitioners in the the field.