In a scenario where you need to monitor the performance of a deployed model in production, explain how you would use MLflow Model Monitoring to detect and alert on data drift and other issues, and discuss the benefits of using MLflow Model Monitoring for production model monitoring.

Simulated

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.

93.8%

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

0.0%

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

0.0%

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

6.3%

Databricks Certified Machine Learning - Associate