Data drift is unexpected changes in model input data that can lead to model performance degradation. Since your code is built around the characteristics of your data, it is important to detect data drift when it occurs.

Evidently allows you to do exactly in a few lines of Python code. In the code below, I use Evidently to detect changes in feature distribution.

Code to create the report above.

Link to Evidently.