40
Models
5M
Items
40M
Users
The problem
- Recommendation quality quietly degrades and nobody notices for weeks.
- Each experiment is a one-off; results can't be compared or repeated.
- Deploys are manual and risky — a bad model hits everyone at once.
- There's no fast way to retrain when tastes shift.
Your mission
Stand up an The practices that take a model from a notebook to reliable production — pipelines, versioning, deployment, and monitoring. platform that ships recommendations safely and retrains as behaviour changes.
Components
0/10 placedTraining0/3
Data0/2
Registry0/1
Serving0/2
Monitoring0/2
Architecture Canvas
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