DoorDash builds ML Workbench to streamline internal machine learning operations and feature management
DoorDash ML practitioners lacked a centralized hub for ML lifecycle tasks, relying on multi-step CLI processes and tedious local workflows for routine tasks like checking feature upload status and validating feature values in production.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Daily feature freshness check
Model owners perform daily checks to ensure feature freshness.
Tools used
ML PortalReactPrismfabricatorDagitRedisExperimentation PlatformMetrics Platform
Outcome
ML Workbench greatly reduced operational overhead for feature store queries and feature upload status checks, with data scientists reporting time savings and improved cross-functional collaboration on feature validation.
What failed first
The earlier ML Portal was built on Python Flask and HTML and suffered from poor information architecture and a difficult UI; users could not discover available capabilities and engineers could not use it to accelerate their own work.