If you have read our previous article, ML Education at Uber: Frameworks Inspired by Engineering Principles, you have seen several examples of how Uber benefits from applying Engineering Principles to drive the ML Education Program’s content design and …
The post ML Education at Uber: Program Design and Outcomes appeared first on Uber Engineering Blog.
At Uber, millions of machine learning (ML) predictions are made every second, and hundreds of applied scientists, engineers, product managers, and researchers work on ML solutions daily.
Uber wins by scaling machine learning. We recognize org-wide that a …
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Latin America is a rich cultural region, known for its world-renowned gastronomy, its abundant biodiversity, and its welcoming population. However, socio-economic inequality has been a challenge for the region, and is generally considered a major …
The post Uber’s Real-Time Document Check appeared first on Uber Engineering Blog.
At Uber, magical customer experiences depend on accurate arrival time predictions (ETAs). We use ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Traditional routing engines compute ETAs by dividing up the road network …
The post DeepETA: How Uber Predicts Arrival Times Using Deep Learning appeared first on Uber Engineering Blog.
Uber is a worldwide marketplace of services, processing thousands of monetary transactions every second. As a marketplace, Uber takes on all of the risks associated with payment processing. Uber partners who use the marketplace to provide services are paid …
The post Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop appeared first on Uber Engineering Blog.
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 kinds of business units. These services are deployed across different cloud platforms and data centers …
The post Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling appeared first on Uber Engineering Blog.
Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases at Uber, spanning from optimizing marketplace dynamic pricing policies for Freight, improving times of …
The post Elastic Distributed Training with XGBoost on Ray appeared first on Uber Engineering Blog.
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 more, algorithms exploit parallelism and rely on distributed training to process an enormous amount of …
The post Fiber: Distributed Computing for AI Made Simple appeared first on Uber Engineering Blog.
At Uber Advanced Technologies Group (ATG), we leverage deep learning to provide safe and reliable self-driving technology. Using deep learning, we can build and train models to handle tasks such as processing sensor input, identifying objects, and predicting where …
The post Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine appeared first on Uber Engineering Blog.
How did the pedestrian cross the road?
Contrary to popular belief, sometimes the answer isn’t as simple as “to get to the other side.” To bring safe, reliable self-driving vehicles (SDVs) to the streets at Uber Advanced Technologies Group (ATG)…
The post Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning appeared first on Uber Engineering Blog.
