model-monitoring-patterns

What problem does it solve?

This Skill addresses the critical need to monitor machine learning models in production for performance degradation, data drift, and concept drift, ensuring models remain reliable and accurate over time.

Core Features & Use Cases

• Data Drift Detection: Identifies shifts in input data distributions using metrics like PSI, KS-test, and Wasserstein distance.
• Performance Estimation: Estimates model performance (e.g., AUC, F1) even when ground truth labels are delayed, using techniques like NannyML's CBPE.
• Alerting & Reporting: Generates reports and alerts for significant drift or performance drops, integrating with tools like Evidently and Prometheus.
• Use Case: Automatically detect when the distribution of user demographics in your recommendation system changes significantly, potentially impacting model accuracy, and trigger an alert for investigation.

Quick Start

Use the model monitoring patterns skill to generate a drift report comparing reference data to current production data.