How do you measure the drift between training and real-world data?

Example Concept: One common method to measure data drift is to use statistical tests such as the Kolmogorov-Smirnov test for continuous variables or the Chi-Square test for categorical variables. These tests compare the distribution of features in the training dataset against the real-world data. Additionally, metrics like Population Stability Index (PSI) can quantify the extent of drift by measuring changes in the distribution of a variable over time. Monitoring these metrics helps identify when retraining or model adjustments are necessary.