Model Monitoring

What problem does it solve?

This Skill addresses the critical need to ensure Machine Learning models remain accurate and reliable in production by detecting and alerting on issues like data drift and performance degradation.

Core Features & Use Cases

• Data Drift Detection: Identifies changes in input data distributions using statistical tests (Kolmogorov-Smirnov, Chi-squared, PSI) and libraries like Evidently AI.
• Performance Monitoring: Tracks key metrics (accuracy, precision, recall, latency) and compares them against baselines.
• Alerting System: Configures rules to notify stakeholders via channels like Slack when predefined thresholds for drift or performance drops are breached.
• Use Case: Automatically monitor a deployed fraud detection model. If the distribution of transaction features changes significantly (data drift) or the model's accuracy drops below 90% (performance degradation), the system triggers a critical alert to the MLOps team.

Quick Start

Use the Model Monitoring skill to detect data drift in the production data compared to the training data.