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DeepETA: How Uber Predicts Arrival Times Using Deep Learning

At Uber, magical customer experiences depend on accurate arrival time predictions (ETAs). We use ETAs to calculate fares, estimate pickup times, match riders to...

Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop

Introduction Uber is a worldwide marketplace of services, processing thousands of monetary transactions every second. As a marketplace, Uber takes on all of the risks...

Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling

Introduction Capacity is a key component of reliability. Uber's services require enough resources in order to handle daily peak traffic and to support our different...

Elastic Distributed Training with XGBoost on Ray

Introduction Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases...

Fiber: Distributed Computing for AI Made Simple

Project Homepage: GitHub Over the past several years, increasing processing power of computing machines has led to an increase in machine learning advances. More and...

Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine

Developed by Uber ATG, Neuropod is an abstraction layer that provides a universal interface to run models across any deep learning framework.

Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine...

Uber ATG's self-driving vehicles measure a multitude of possible scenario variations to answer the age-old question: "how does the pedestrian cross the road?"

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.

Announcing a New Framework for Designing Optimal Experiments with Pyro

Uber AI released a new framework on top of Pyro that lets experimenters seamlessly automate optimal experimental design (OED) for quicker model iteration.

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Building upon our existing open-ended learning research, Uber AI released Enhanced POET, a project that incorporates an improved algorithm and allows for more diverse training environments.
Uber ATG self-driving cars

Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for...

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.

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.
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.

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.

Controlling Text Generation with Plug and Play Language Models

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.

Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

By integrating graph learning techniques with our Uber Eats recommendation system, we created a more seamless and individualized user experience for eaters on our platform.

Uber Goes to NeurIPS 2019

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.

Announcing the 2020 Uber AI Residency

Uber's 2020 AI Residency will focus on initiatives related to our self-driving car project through Uber Advanced Technology Group (ATG).

Get to Know Uber ATG at ICCV, CoRL, and IROS 2019

Attending ICCV, CoRL, or IROS 2019? Learn about Uber ATG's recent research in artificial intelligence by checking out our workshops, posters, and keynotes.

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.