In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.