ML monitoring encompasses establishing alerts on key model performance metrics such as accuracy and drift. The practice helps identify precisely when the model performance started diminishing. Monitoring the automated workflows helps to maintain the required accuracy and keeps transformations error-free.

The different types of ML Monitoring metrics are: 

• Data drift
• Model drift
• Fairness/ Bias monitoring
• Explainability