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Food Discovery with Uber Eats: Building a Query Understanding Engine
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Choice is fundamental to the Uber Eats experience. At any given location, there could be thousands of restaurants and even more individual menu items for an eater to choose from. Many factors can influence their choice. For example, the time of day, their cuisine prefer
From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Data / ML, EngineeringFrom Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial ToolboxMay 29, 2018 / Global Maps are based on our physical world. We create maps using abstract shapes and colors to reveal geographic patterns and te
Uber Privacy
This policy applies to users of Uber's services anywhere in the world, including users of Uber's apps, websites, features or other services. This policy describes how Uber and its affiliates collect and use personal information to provide our services. This policy applies to all users of our apps, websites, features or other services anywhere in the world, unless covered by a separate privacy poli
Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool
Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool Operating in more than 600 cities worlwide, Uber leverages aggregate data about user preferences to improve rider and driver experiences. Over time, these anonymously aggregated data points generate millions of time series, which can be viewed broadly or at a deep level of time granularity. These time series forecasts a
Engineering a Job-based Forecasting Workflow for Observability Anomaly Detection
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more At Uber, we combine real-time systems monitoring with intelligent alerting mechanisms to ensure the availability and reliability of our apps. In our push to empower our engineers to author more accurate alerts, Uber’s Observability Applications team sought to introduce
Introducing Nanoscope: An Extremely Accurate Method Tracing Tool for Android
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Last October, Uber’s Mobile Engineering team kicked off an effort to improve app performance, and we’ve made great progress so far with speedups of well over 50 percent for some of our key transitions. Early on, we learned that certain classes of performance issues are
Rethinking GPS: Engineering Next-Gen Location at Uber
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Location and navigation using global positioning systems (GPS) is deeply embedded in our daily lives, and is particularly crucial to Uber’s services. To orchestrate quick, efficient pickups, our GPS technologies need to know the locations of matched riders and drivers,
Welcoming the Era of Deep Neuroevolution
Safe mutations through gradient computations In a separate paper, we show how gradients can be combined with neuroevolution to improve the ability to evolve recurrent and very deep neural networks, enabling the evolution of DNNs with over one hundred layers, a level far beyond what was previously shown possible through neuroevolution. We do so by computing the gradient of network outputs with resp
Scaling Uber’s Hadoop Distributed File System for Growth
EngineeringScaling Uber’s Apache Hadoop Distributed File System for GrowthApril 5, 2018 / Global Three years ago, Uber Engineering adopted Hadoop as the storage (HDFS) and compute (YARN) infrastructure for our organization’s big data analysis. This analysis powers our services and enables the delivery of more seamless and reliable user experiences. We use Hadoop for both batch and streaming analyt
Engineering Uber’s On-Call Dashboard
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber’s on-call engineers facilitate seamless experiences for riders and drivers worldwide by maintaining the 24/7 reliability of our apps. To execute on this reliability, however, we need to ensure that our on-call teams are set up for success. Until January 2016, our o
Introducing QALM, Uber’s QoS Load Management Framework
EngineeringIntroducing QALM, Uber’s QoS Load Management FrameworkMarch 22, 2018 / Global Much of Uber’s business involves connecting people with people, making the reliability of our customer platform crucial to our success. The customer platform supports everything from ridesharing and Uber Eats, to Uber Freight and Uber for Business. Our platform team owns four services with thousands of hosts,
Designing the Uber kiosk
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Over the past few decades, the advent of online commerce negatively impacted brick-and-mortar stores, but now the pendulum is swinging back. With millennials seeking more real-world experiences, some companies have evolved how they reach out to consumers in public space
Denial By DNS: Uber’s Open Source Tool for Preventing Resource Exhaustion by DNS Outages
Denial By DNS: Uber’s Open Source Tool for Preventing Resource Exhaustion by DNS Outages In June 2016, an unresponsive third-party Domain Name System (DNS) server caused an outage of a legacy login service, affecting riders and drivers trying to access the Uber app. While the issue was mitigated in minutes, discovering why this happened was far more challenging. As part of Uber’s commitment to arc
Unifying Mobile Onboarding Experiences at Uber
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber is committed to creating safer and more reliable transportation solutions for users worldwide. To fulfill this vision, mobile onboarding—an app’s entry point—must be quick, painless, and easy-to-navigate. Before we released Uber’s new rider app in November 2016, ou
Queryparser, an Open Source Tool for Parsing and Analyzing SQL
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more In early 2015, Uber Engineering migrated its business entities from integer identifiers to UUID identifiers as part of an initiative towards using multiple active data centers. To achieve this, our Data Warehouse team was tasked with identifying every foreign-key relati
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