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Tag: ML

Profiles in Coding: Diana Yanakiev, Uber ATG, Pittsburgh

Self-driving cars have long been considered the future of transportation, but they’re becoming more present everyday. Uber ATG (Advanced Technologies Group) is at the forefront of this technology, helping bring safe, reliable self-driving vehicles...

Meta-Graph: Few-Shot Link Prediction Using Meta-Learning

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.

Building a Backtesting Service to Measure Model Performance at Uber-scale

We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.

Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning

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 AI in 2019: Advancing Mobility with Artificial Intelligence

Uber AI in 2019: Advancing Mobility with Artificial Intelligence

In 2019, Uber AI built tools and systems that leverage ML to improve location accuracy and enhance real-time forecasting, among other applications on our platform.

Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.

Evolving Michelangelo Model Representation for Flexibility at Scale

To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.

Three Approaches to Scaling Machine Learning with Uber Seattle Engineering

At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.

Introducing EvoGrad: A Lightweight Library for Gradient-Based Evolution

Uber AI Labs releases EvoGrad, a library for catalyzing gradient-based evolution research, and Evolvability ES, a new meta-learning algorithm enabled by this library.

Gaining Insights in a Simulated Marketplace with Machine Learning at Uber

Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.

Improving Uber’s Mapping Accuracy with CatchME

CatchMapError (CatchMe) is a system that automatically catches errors in Uber's map data with anonymized GPS traces from the driver app.

Data Science at Scale: A Conversation with Uber’s Fran Bell

We spoke to Data Science Director Fran Bell about machine learning at Uber and what she finds most challenging—and rewarding—about her work.

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.

Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead

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.

How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN

Metropolis-Hastings Generative Adversarial Networks (GANs) leverage the discriminator to pick better samples from the generator after ML model training is done.

NVIDIA: Accelerating Deep Learning with Uber’s Horovod

Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.

My Journey from Working as a Fabric Weaver in Ethiopia to Becoming a Software...

Samuel Zemedkun reflects on his immigrant experience and how his part-time driving through the Uber platform funded his education and inspired his decision to join the company.

Scaling Machine Learning at Uber with Michelangelo

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.

Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development

Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.

Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System

One-click chat, the Uber driver app's smart reply system, leverages machine learning to make in-app messaging between driver-partners and riders more seamless.

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