This blog describes how we apply reinforcement learning techniques to make the Uber network more efficient, helping the world move and creating magical experiences for riders and drivers. We discuss how we apply reinforcement learning in our matching algorithm to improve driver and demand balance in our mobility marketplace.| Uber Blog
Uber uses reinforcement learning to optimize matching and better balance drivers with demand in real time. By considering long-term outcomes, we improve efficiency and increase driver earnings by proactively aligning drivers with demand.| Serbia | Latest News & Stories | Uber Blog
Uber uses reinforcement learning to optimize matching and better balance drivers with demand in real time. By considering long-term outcomes, we improve efficiency and increase driver earnings by proactively aligning drivers with demand.| United Kingdom | Latest News & Stories | Uber Blog
With the introduction of Model Excellence Scores at Uber, we're setting a new standard for measuring, monitoring, and maintaining ML model quality–read how this innovative approach aims to enhance ML governance and provide clearer insights.| Uber Blog
Apache Hive™ on Apache Spark™ has been the preferred engine for ETL workloads at Uber. Hive on Spark supports a wide range of use cases across various verticals like compliance, financial reporting, planning, forecasting, fraud, and risk analysis. Before the migration, there were about 18,000 Hive ETL workflows generating around 5 million queries per month, contributing to significant percentage of Uber’s total Yarn usage. Additionally, Hive was used for interactive use cases, handling ...| Uber Blog
Uber processes vast amounts of data daily—across multiple verticals—using technologies like Apache Hadoop™, Apache Hive™, and Apache Spark™. Each data team at Uber must operate within resource constraints while managing ever-growing data volumes. Our team, CDS (Compliance Data Store) serves as Uber’s central repository for regulatory reporting. We share data with regulators in accordance with applicable laws and requirements.. Moreover, managing this extensive data poses significa...| Uber Blog
Uber processes vast amounts of data daily—across multiple verticals—using technologies like Apache Hadoop™, Apache Hive™, and Apache Spark™. Each data team at Uber must operate within resource constraints while managing ever-growing data volumes. Our team, CDS (Compliance Data Store) serves as Uber’s central repository for regulatory reporting. We share data with regulators in accordance with applicable laws and requirements.. Moreover, managing this extensive data poses significa...| Uber Blog
Genie is Uber’s internal on-call copilot, designed to provide real-time support for thousands of queries across multiple help channels in Slack®. It enables users to receive prompt responses with proper citations from Uber’s internal documentation. It also improves the productivity of on-call engineers and subject matter experts (SMEs) by reducing the effort required to address common, ad-hoc queries. While Genie streamlines the development of an LLM-powered on-call Slack bot, ensuring t...| Uber Blog
Uber's data lake is migrating to the cloud! Learn how they're tackling security challenges and scaling the system to handle massive amounts of data while ensuring a seamless transition for users.| Uber Blog