Tag: Uber ATG
To celebrate International Women's Day, we spoke with women from across the company whose work helps deliver impactful experiences for Uber users worldwide.
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.
Meet a few of the engineers from Uber's Boulder, Colorado office, working on everything from maps to new mobility to large-scale distributed systems.
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.
Uber ATG Web Platform intern Anat Kleiman shares her advice for testing React version 16 components when altering application logic.
As head of Uber's Advanced Technologies Center in Paris, Francois Sillion and his team are responsible for supporting the R&D behind Uber Air, our effort to add a third dimension to our platform using flying vehicles.
A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In our development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe.
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 Self-Driving Cars to Optimizing Claims Efficiency: My Unconventional Journey to Insurance Engineering
In this article, engineering manager Lili Kan reflects on her decision to lead Uber's Insurance Engineering team and discusses the challenges—and opportunities—of building insurance products for our platform.
Uber's Head of Urban Computing & Visualization reflects on his team's work visualizing data to better understand urban mobility in 2018—and beyond.
We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.
Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.
On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.
Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.
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