Rick Boone, Strategic Advisor for Uber's Core Infrastructure group, talks about his journey from his work in site reliability to his current role in long-term planning for infrastructure health and scalability.
Uber engineers share their learnings on how to tune a Java Virtual Machine so as to avoid long pauses and other issues with garbage collection.
The Uber Seattle Tech team is responsible for building a diverse range of technologies, from developer tools to our data platform architecture.
We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.
Improving the performance and developer velocity for the Uber Eats web application involved a complete rewrite, developing a new architecture and using Fusion.js.
In October 2019, Uber hosted our second annual Moving The World With Data meetup, showcasing some of our most interesting data science challenges in 2019.
We implemented a Kappa architecture at Uber to effectively backfill streaming data at scale, ensuring accurate data in our platform.
First introduced by Uber Engineering in January 2019, Manifold is a visual debugging tool that enables users to quickly identify performance issues in machine learning models.
As part of Uber Visualization's all-team hackathon, we built SpeedsUp, a project using machine learning to process average speeds across a city, cluster the results, and overlay them on a street map.
Uber recounts its many engagements with the open source community during 2019, from contributing projects to joining and founding new open source support organizations.
To cap off 2019, the Uber Engineering Blog editors present a selection of our most popular articles covering a range of technical topics, from AI to mobile development.
In 2019, Uber's Infrastructure team built new services and systems to enable resource savings, efficiency gains, and greater resilience across our technology stack.
In 2019, Uber AI built tools and systems that leverage ML to improve location accuracy and enhance real-time forecasting, among other applications on our platform.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Developed by Uber AI Labs, Generative Teaching Networks (GTNs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.
In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
Plug and Play Language Model, introduced by Uber AI Labs, gives NLP practitioners the flexibility to plug in one or more simple attribute models into a large, unconditional language model.
By integrating graph learning techniques with our Uber Eats recommendation system, we created a more seamless and individualized user experience for eaters on our platform.