Uber's IT Engineering team scaled mobile device management on macOS by leveraging open source tools and custom API-driven Chef cookbooks.
Our driver app's new server-driven preferences section enables driver-partners to customize their experiences to make the app better fit into their lives.
Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.
During an October 2018 meetup, members of our Women in Statistics, Data, Optimization, and Machine Learning (WiSDOM) group presented on their technical work at Uber.
Brian Hsieh, Uber's Open Source program lead, reflects on open source accomplishments, project launches, and collaborations in 2018.
Uber built Makisu, our open source Docker image builder, to enable the quick, reliable generation of Dockerfiles in Mesos and Kubernetes ecosystems.
Montezuma’s Revenge Solved by Go-Explore, a New Algorithm for Hard-Exploration Problems (Sets Records on...
Uber AI Labs introduces Go-Explore, a new reinforcement learning algorithm for solving a variety of challenging problems, especially in robotics.
Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.
Engineering Sustainability: An Interview with Uber’s Head of Information Technology, Shobhana Ahluwalia
We sat down with Uber's Head of Information Technology to discuss her journey to tech services, what she finds most challenging about her work at Uber, and how her team is setting the company up for success.
Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.
Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.
Technical writer and former intern Shannon Brown explains her work and answers common questions about this important role in Uber’s engineering organization.
Joe Zhou, the 7th iOS engineer on the Uber Eats team, offers advice for those considering taking the plunge into programming.
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 developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
Fusion.js, Uber's new open source web framework, supports modern features and integrations that make it easy to build lightweight, high-performing apps for the web.
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.
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.
uRate empowers both Uber employees and customers to provide quick and efficient feedback on tools and products, enabling engineers to build more responsive services.
Product Manager Zach Singleton talks about how Uber partnered with The Hidden Genius Project to create the Career Prep Program, a one-year course that prepares black male computer science students for careers in tech.
Uber's Data Infrastructure team overhauled our approach to scaling our storage infrastructure by incorporating several new features and functionalities, including ViewFs, NameNode garbage collection tuning, and an HDFS load management service.
Uber's Customer Obsession Engineering team developed new check-in queuing and appointment systems to improve the customer experience for driver-partners at our Greenlight Hubs.
Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool to help neuroevolution researchers better understand this family of algorithms.
Brought to the US when he was 10 years old, DACA gave Benito Sanchez the security to go to college and get a job in technology.
Migrating our Schemaless sharding layer from Python to Go while in production demonstrated that it was possible for us to rewrite the frontend of a massive datastore with zero downtime.
The Uber Insurance Engineering team extended Kafka’s role in our existing event-driven architecture by using non-blocking request reprocessing and dead letter queues (DLQ) to achieve decoupled, observable error-handling without disrupting real-time traffic.
Uber Engineering extended our anomaly detection platform's ability to integrate new forecast models, allowing this critical on-call service to scale to meet more complex use cases.
Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.
Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.
How do you overcome imposter syndrome? Act with confidence, follow your first instinct, and always be learning and teaching.
In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.
Get to know Uber Aarhus Engineering and the work they do behind the scenes to build and maintain our storage and compute platforms.
As we approach the New Year, Uber Open Source revisits some of Uber Engineering's most popular projects from 2017.
Up for the challenge of developing at unprecedented scale? First, learn what it takes to master the technical interview process at Uber.
Uber Engineering built Denial by DNS, our open source solution for preventing DoS by DNS outages, to facilitate more reliable experiences on Uber's apps, no matter how users choose to access them.
Uber’s Customer Obsession team builds tools that make the customer support experience quicker and more seamless for users across our services.
In this article, members of Uber Bangalore Engineering discuss their role in building reliable transportation systems at scale for India—and beyond.
Uber Engineering built and open sourced NullAway, our fast and practical tool for eliminating NPEs, to help others deploy more reliable Android apps.
Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.