scientific-model-monitoring

What problem does it solve?

In production ML, models drift and degrade, requiring automated surveillance to maintain predictive quality. This skill provides end-to-end monitoring for data drift, feature drift, concept drift, and A/B test analysis, enabling timely alerts and governance.

Core Features & Use Cases

• Data drift detection: Integrates Evidently/NannyML to quantify drift between reference and current data.
• Concept drift and performance monitoring: Detect changes in P(Y|X) and model degradation, with alerting and retraining triggers.
• A/B testing statistics: Compare models in production and compute statistically significant winners to guide deployment decisions.
• Model registry and governance: Track versions, experiments, and triggers for redevelopment cycles.

Quick Start

Integrate this monitoring pipeline with your deployed model and start data drift, concept drift, and performance monitoring immediately.