🧠 AI & Machine Learning · State Machine
ML Model Lifecycle
The states a machine learning model moves through, from experiment to production to retirement, including rollback when live performance drops.
Drawing diagram…
What this diagram shows
- Nothing reaches production without validation and approval
- Shadow mode tests a model on live traffic without affecting users
- Drift in live data sends the model back for retraining
Prompt used
State machine for an ML model: Experiment, Trained, Validated (passes metric thresholds), Approved, Shadow (runs on live traffic without serving answers), Production, Degraded when drift is detected, then Retraining, and Retired when replaced. A failed validation goes back to Experiment and a bad shadow result is rejected.
Mermaid code
stateDiagram-v2 [*] --> Experiment Experiment --> Trained: Training run finished Trained --> Validated: Metrics above threshold Trained --> Experiment: Metrics too low Validated --> Approved: Reviewer sign-off Approved --> Shadow: Deploy without serving Shadow --> Production: Live results match Shadow --> Rejected: Live results worse Production --> Degraded: Data drift detected Degraded --> Retraining Retraining --> Trained Production --> Retired: Replaced by new version Rejected --> [*] Retired --> [*]
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