Doordash optimizes real-time notifications with the frontend events by leveraging streaming processing.
DoorDash's in-house multi-layer cache, used to unlock our high-performance services across DoorDash
Solutions for dealing with sample ratio mismatch.
Learn how DoorDash build a metrics layer to enable consistent metrics and democratized decision making for experimentation
While building our ML feature store we learned that a mix of databases yields significant gains in efficiency and operational simplicity.
Figuring out how to balance our experimentation speed with the necessary controls to maintain trust is never easy. Learn DoorDash's approach.
Learn how DoorDash developed a switchback testing method to perform incrementality testing on an app marketplace ad platform
Data preparation, represents The vast majority of work in developing machine learning models, learn how to make things easier
Learn how DoorDash build a platform to process billions of events from different data sources, quickly, consistently and reliably
Learn how DoorDash crowdsources data from a variety of sources to help predict realtime inventory for our new connivence and grocery product
When A/B testing is not recommended we can still quickly implement a new feature and measure its effects in a data-driven way.
Learn how DoorDash was able to test uniformly according to established best practices and reuse complex statistical methods with Dash AB
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