Abstract: A method for creating machine learning model performance alerts showing the drifting of functions is described herein. The method starts by creating the initial machine learning model using a training data set. This initial machine learning model is then used in production, and the model is updated to account for the production data. To assure the quality of the updated machine learning model, test data results from the initial machine learning model is compared to the results from the updated machine learning model. Each feature is checked to see if the difference is within a p-value and whether the confidence intervals overlap. If not, an alert is generated to take action on the model.