DriftH

Ensure Model Resilience and Data Integrity through Real-Time Drift Monitoring

Delivering consistent performance with predictive and metric-guided insights.

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DriftH

Overview of multiple models' performance
DriftH enables its users to overview model performance with common metrics such as model lift and model target ratio.
Surveillance of degredation rate
DriftH metric, created by SnA, shows degradation rate of your model in a timeseries graph, including Next period's DriftH metric, which is predicted.
Model quality measurement
DriftH shows variables’ quality over different periods and creates a violation alert. DriftH also allows you to decide the change ratio of violation alert.

Why DriftH?

Ensuring Model Resilience and Data Integrity through Real-Time Drift Monitoring. Delivering Consistent Performance with Predictive, Metric-Guided Insights

Continuous Model Monitoring

DriftH continuously tracks multiple model performance metrics — Lift, Confusion Matrix, Accuracy, Precision, F1 Score, Recall, AUC, and Gini — ensuring that model behavior is always visible and measurable.

Proactive Drift Detection

Using the proprietary DriftH metric, the system predicts potential deviations in upcoming scoring, allowing teams to act before model performance deteriorates. Early detection minimizes risk and ensures stable decision-making.

Data Integrity Assurance

DriftH monitors the stability of input variables, highlighting violations and unexpected deviations in data patterns. This ensures that input datasets remain consistent, reliable, and free from integrity issues.

Actionable Alerts and Insights

Violation warnings provide clear, actionable guidance on which variables deviate beyond defined thresholds. Teams can quickly investigate, adjust, and maintain model robustness and compliance.