Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
The UberEATS Restaurant Manager gives restaurant partners insight into their business by measuring customer satisfaction, sales, and service quality.
Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.
Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.
In this article, we discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.
How Uber Engineering re-architected the content delivery feed and backend ecosystem of our new driver app to deliver an enhanced user experience.
Uber Engineering's data processing platform team recently built and open sourced Hudi, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hudi powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.
In this article, we take a look at Euclid, Uber Engineering's Hadoop and Spark-based in-house marketing platform.