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.
Not Exactly a Linter (NEAL) takes code reviews one step closer to full automation by allowing engineers to write custom syntax-based rules in any language.
Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.
To mark the two-year anniversary of Uber Eats, Android engineer Hilary Karls discusses how her team's commitment to "playing the perfect game" resulted in one of Uber’s most successful products.
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.
Uber's mobile engineers leverage code generation to make our applications more reliable and boost developer productivity.
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.
To ring in the New Year, the Uber Engineering Blog shares some of our editor's picks for 2017.
Up for the challenge of developing at unprecedented scale? First, learn what it takes to master the technical interview process at Uber.
Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning
Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.