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At Uber, we use marketplace algorithms to connect drivers and riders. Before the algorithms roll out globally, Uber fully tests and evaluates them to create an optimal user experience that maps to our core marketplace principles.

To make product …

The post Gaining Insights in a Simulated Marketplace with Machine Learning at Uber appeared first on Uber Engineering Blog.

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Machine learning models perform a diversity of tasks at Uber, from improving our maps to streamlining chat communications and even preventing fraud.

In addition to serving a variety of use cases, it is important that we make machine learning …

The post No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox appeared first on Uber Engineering Blog.

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Reliable transportation requires a robust map stack that provides services like routing,  navigation instructions, and ETA calculation. Errors in map data can significantly impact services, leading to a suboptimal user experience. Uber engineers use various sources of feedback to identify …

The post Improving Uber’s Mapping Accuracy with CatchME appeared first on Uber Engineering Blog.

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Machine learning (ML) pervades many aspect of Uber’s business. From responding to customer support tickets, optimizing queries, and forecasting demand, ML provides critical insights for many of our teams.

Our teams encountered many different challenges while incorporating …

The post Accessible Machine Learning through Data Workflow Management appeared first on Uber Engineering Blog.

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Fran Bell has always been a scientist; theorizing, modeling and testing how the world works. An ever-curious child, she was fascinated by the natural world, poring over biology and chemistry books, but was never satisfied with just knowing; she …

The post Data Science at Scale: A Conversation with Uber’s Fran Bell appeared first on Uber Engineering Blog.

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Over the past several years, artificial intelligence (AI) has become an integral component of many enterprise tech stacks, facilitating faster, more efficient solutions for everything from self-driving vehicles to automated messaging platforms. On the AI spectrum, deep probabilistic programming, a …

The post Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro Team appeared first on Uber Engineering Blog.

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Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support

The post Introducing Ludwig, a Code-Free Deep Learning Toolbox appeared first on Uber Engineering Blog.

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By Lezhi Li and Yang Wang

Machine learning (ML) is widely used across the Uber platform to support intelligent decision making and forecasting for features such as ETA prediction and fraud detection. For optimal results, we invest a lot …

The post Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber appeared first on Uber Engineering Blog.

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By Rui Wang, Joel Lehman, Jeff Clune*, & Kenneth O. Stanley*

*co-senior authors

POET: Endlessly Generating Increasingly Complex & Diverse Learning Environments and their Solutions - YouTube

We are interested in open-endedness at Uber AI Labs because it offers the potential for generating a diverse and ever-expanding curriculum for machine learning entirely on its …

The post POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer appeared first on Uber Engineering Blog.

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By Molly Vorwerck

For Alex Sergeev, the decision to open source his team’s new distributed deep learning framework, Horovod, was an easy one.

Tasked with training the machine learning models that power the sensing and perception systems used by …

The post Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead appeared first on Uber Engineering Blog.

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