Entropy

Monitoring and Model Performance Tracking

Machine Learning models require continuous monitoring to ensure they perform optimally in dynamic environments. Model drift, where accuracy degrades over time due to changing data patterns, must be detected and mitigated.

Monitoring tools like MLflow, Prometheus, and Grafana help track key performance indicators (KPIs) such as prediction accuracy, inference time, and resource utilization. Regular model retraining is often necessary to maintain performance levels.