How to detect data drift in production Machine Learning models using Python?

Once a machine learning model is deployed to production, input features tend to shift away from the training distribution due to seasonal trends, user behavior changes, or upstream pipeline alterations. Identifying this data drift before model accuracy degrades is crucial for maintaining AI service stability.