scientific-model-monitoring

Data drift and concept drift are detected by computing drift metrics between reference and current data to monitor changes in P(Y|X). This skill quantifies feature drift and model degradation to maintain predictive quality and raise timely alerts.

Performance degradation is monitored by tracking changes in P(Y|X) and predictive quality over time. The system evaluates these drift metrics to automatically trigger retraining cycles and log results for governance and auditability.

A/B testing for deployed ML models compares production model versions to compute statistically significant winners. This analysis guides deployment decisions by evaluating which model maintains better predictive quality under live data conditions.

Yes, data drift detection integrates with Evidently and NannyML to quantify drift between reference and current data. These integrations enable automated surveillance of feature drift and model degradation in production ML systems.

Yes, model registry and governance tracking monitors versions, experiments, and retraining triggers for redevelopment cycles. The pipeline logs drift metrics and alerts to maintain auditability and governance for deployed ML systems.

ML models degrade over time due to data drift and concept drift where production data distributions change from reference data. Automated surveillance detects these shifts, raises alerts, and triggers retraining to maintain predictive quality.