Model Monitoring

Model Monitoring

Overview

Monitoring deployed machine learning models ensures they continue to perform well in production, detecting data drift, concept drift, and performance degradation.

When to Use

• When models are deployed in production environments serving real users
• When detecting data drift or concept drift in input features
• When tracking model performance metrics over time
• When ensuring model reliability, accuracy, and operational health
• When implementing ML observability and alerting systems
• When establishing thresholds for model retraining or intervention

Monitoring Components

• Performance Metrics: Accuracy, latency, throughput
• Data Drift: Distribution changes in input features
• Concept Drift: Changes in target variable relationships
• Output Drift: Changes in prediction distribution
• Feature Drift: Individual feature distribution changes
• Anomaly Detection: Unusual samples in production

Monitoring Tools