Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations. Neural networks, an important tool for processing data in a variety of industries, grew from an academic research area to a cornerstone of industry over the last few years. Convolutional Neural Networks (CNNs) have […]
Uber, like most large technology companies, relies extensively on metrics to effectively monitor its entire stack. From low-level system metrics, such as memory utilization of a host, to high-level business metrics, including the number of Uber Eats orders in a particular city, they allow our engineers to gain insight into how our services are operating […]
To ensure the stable, scalable growth of our diverse tech stack, we leverage a microservices-oriented architecture, letting engineers deploy thousands of services on a dynamic, high-velocity release cycle. These services enable new features to greatly improve the experiences of riders, drivers, and eaters on our platform. Although this paradigm supported hypergrowth in both scale and […]
During a September 2018 meetup, Uber’s Payments Platform team discusses how this technology supports our company’s growth through an active-active architecture, exactly-once payment processing, and scalability across businesses. From ridesharing, food delivery, and new modalities to freight, healthcare, and business travel, our technology helps create billions of moments of human connection around the world. Building […]
Uber’s many data flows required modeling the data associated with a specific task, such as a rider trip, into a state machine. The state machine lets engineers focus on just the events needed to successfully accomplish a trip. In one sense, Uber’s challenge of efficiently matching riders and drivers in the real world comes down […]