What is ML model monitoring and why is it important?

What is ML model monitoring and why is it important?

ML model monitoring involves tracking the performance of machine learning models in production to ensure they function as expected.

What are the key metrics to monitor for ML models?

Key metrics include accuracy, precision, recall, F1 score, and model drift, which indicates changes in data distribution.

How often should I monitor my ML models?

Monitoring frequency depends on the application, but it's generally recommended to check performance regularly, such as daily or weekly.

What tools can I use for ML model monitoring?

Popular tools include Prometheus, Grafana, and specialized platforms like DataRobot and MLflow.

Can you explain model drift and how to detect it?

Model drift occurs when the statistical properties of the target variable change. It can be detected using techniques like monitoring performance metrics over time.