Maps make up the bedrock of Uber's transportation solutions. Find out how we ensure the quality of our map data through extensive metrics computation, maintaining fidelity to real world locations and pinpointing allowable pick up and drop off locations for riders.
As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.
Uber open sourced JVM Profiler, our distributed profiler, to enable others to seamlessly collect JVM performance and resource usage metrics.
With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
Uber's attorneys explain the intricacies of different types of open source software licenses and intellectual property.
Using GPS and sensor data from Android phones, Uber engineers develop a state model for trips taken by Uber Eats delivery-partners, helping to optimize trip timing for delivery-partners and eaters alike.
To detect and prevent fraud, Uber brings to bear data science and machine learning, analyzing GPS traces and usage patterns to identify suspicious behavior.
Established in 2014 as one of Uber's first distributed engineering sites, Uber Sofia is home to our Tax & Compliance Engineering team, a group responsible for developing the technologies that power our key reporting and compliance services.
Uber engineers share how we process search terms for our Uber Eats service, using query understanding and expansion to find restaurants and menu items that best match what our eaters want.
On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.
Making JUMP Bikes' semi-dockless electric bicycles available on Uber's platform not only added a popular new transportation type for Uber riders, but also marked an important step in how we can use our technology to broaden transportation options.
From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox
Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.
Shan He, the technical lead on Uber's kepler.gl framework, discusses her journey to data visualization and why she believes open source is such an important part of her team's work.
deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.
Uber’s Observability Applications team overhauled our anomaly detection platform’s workflow to enable the intuitive and performant backfilling of forecasts, paving the way for more intelligent alerting.
Customer-focused Engineering at Uber: A Q&A with Jörg Heilig, VP of Ridesharing and Eats Engineering
In this interview, Uber Vice President of Engineering for Ridesharing and Eats Jörg Heilig talks about taking a leadership role in a large engineering organization with a broad portfolio and the priorities being set for 2018.
uRate empowers both Uber employees and customers to provide quick and efficient feedback on tools and products, enabling engineers to build more responsive services.