12
Models
300+
Features
40M
Predictions/day
The problem
- A data scientist trains the fraud model by hand in a notebook.
- Nobody can reproduce last quarter's model or roll back a bad one.
- The live model silently When the real world changes and a once-good model slowly gets worse — like a map that's no longer accurate. as fraud patterns change.
- Training and serving use different feature logic, so predictions are off.
Your mission
Build a production The practices that take a model from a notebook to reliable production — pipelines, versioning, deployment, and monitoring. pipeline that ships fraud models reliably and keeps them accurate.
Components
0/11 placedData0/3
Training0/3
Registry0/1
Serving0/2
Monitoring0/2
Architecture Canvas
+
Drag components here to design the landing zone
or tap any component to add it