Live model monitoring
A simulated process emits observations around hidden class centres. A PyTorch classifier predicts each batch; when accuracy stays below the threshold, it retrains on the recent window and redeploys.
Live accuracy per interval
Share of each new batch the deployed model classifies correctly. Shaded spans mark data drift; vertical rules mark redeployments.
Data in 2-D projection
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- P k current centre
Controls
Trigger drift, retrain, and tune the monitoring policy.
The dashboard is public; controls need an operator session. Sign in
Training runs · validation loss per epoch
Validation is the most recent intervals of the training window. Training stops when validation loss stops improving; the dot marks the epoch whose weights were deployed.
Training parameters
AdamW with early stopping. Changes apply from the next training.
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Event log
Newest first. Every training run is also logged to MLflow.
No events yet.