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 …
In 2017, we introduced Horovod, an open source framework for scaling deep learning training across hundreds of GPUs in parallel. At the time, most of the deep learning use cases at Uber were related to the research and …
At Uber, hundreds of data scientists, economists, AI researchers and engineers, product analysts, behavioral scientists, and other practitioners leverage scientific methods to solve challenges on our platform. From modeling and experimentation to data analysis, algorithm development, and fundamental research, the …
Computing frameworks like Apache Spark have been widely adopted to build large-scale data applications. For Uber, data is at the heart of strategic decision-making and product development. To help us better leverage this data, we manage massive deployments of Spark …
Cherami is a distributed, scalable, durable, and highly available message queue system we developed at Uber Engineering to transport asynchronous tasks. We named our task queue after a heroic carrier pigeon with the hope that this system would be just …