Use MLflow Model Monitoring to log the model's input data and performance metrics, and set up alerts to detect data drift or performance degradation.

Manually monitor the model's performance by periodically checking the logs and metrics.

Use the MLflow UI to create a custom dashboard to visualize the model's performance and detect issues manually.

Use the MLflow Client API to programmatically retrieve the model's performance metrics and analyze them for data drift or other issues.