A need to retrain a machine learning model is determined based on identification of either a new scenario (i.e., new grouping/population of data variables) or a modification to an existing/trained scenario (i.e., addition and/or deletion of data variables in an already trained scenario). Change occurring across all of the data variables used in training the machine learning model are monitored as opposed to monitoring only the data variables on an individual basis. When concurrent change occurs across multiple inter-related data variables, the need to add or modify a scenario (i.e., retrain a machine learning model) is identified.