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Answer: 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.
Option A is the correct approach as it involves using MLflow Model Monitoring to log the model's input data and performance metrics, and set up alerts to detect data drift or performance degradation. Using MLflow Model Monitoring for production model monitoring provides several benefits, such as automated detection of issues, real-time monitoring, and the ability to take proactive actions to address data drift or performance degradation. It also helps in maintaining the reliability and accuracy of the deployed model in production.
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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.
A
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.
B
Manually monitor the model's performance by periodically checking the logs and metrics.
C
Use the MLflow UI to create a custom dashboard to visualize the model's performance and detect issues manually.
D
Use the MLflow Client API to programmatically retrieve the model's performance metrics and analyze them for data drift or other issues.