FIG.06
Distribution Shift
Degradation of model performance over time as the operational environment drifts away from the training distribution.
Failure Domain
Concept Drift
Data Staleness
Temporal Shift
Shift Types:
Distribution Comparison
Comparing Training Baseline vs Production Data.
KL Divergence:
0.42
Wasserstein Dist:
1.85
Status:
DRIFT DETECTED
Monitoring Pipeline
Feature Stats
Marginal distribution checks
KS-Test: Pass
PSI: 0.15 (Warn)
PSI: 0.15 (Warn)
Embedding Drift
Latent space monitoring
MMD: 0.89 (Alert)
Centroid Dist: High
Centroid Dist: High
BREACH
Performance Monitors
Ground truth evaluation
Accuracy: -12% drop
F1 Score: 0.72 < 0.80
F1 Score: 0.72 < 0.80
Response Playbook
Recalibration
Adjust decision thresholds without altering model weights.
Retraining Trigger
Initiate pipeline with recent production data samples.
Rollback
Revert to previous stable checkpoint if degradation is severe.
Fallback Model
Engage simpler, robust heuristic model during outage.
Trust Calibration
Mapping Confidence vs Error Rate
Calibrated
Overconfident
Current State:
MISALIGNED