What is the purpose of a model monitoring system post-deployment?

What is the purpose of a model monitoring system post-deployment?

Step 1: Understand that a model monitoring system is used after a machine learning model is deployed.
Step 2: Know that the system tracks how well the model is performing over time.
Step 3: Learn that it checks for performance metrics, which are numbers that show how accurate or effective the model is.
Step 4: Recognize that the system can detect issues, like data drift, which happens when the data the model sees changes over time.
Step 5: Understand that it also looks for model degradation, which means the model is not performing as well as it used to.
Step 6: Realize that the purpose of this monitoring is to ensure the model continues to work correctly and to identify any problems early.

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