Abstract: Apparatuses, systems, and techniques to classify computing devices as idle or busy using a machine learning (ML) model based on power consumption data collected non-intrusively are described. One method receives power consumption data for a computing device from a service processor operatively coupled to the computing device. The method determines a set of features from the power consumption data for a first time period. The method classifies, using an ML model and the set of features, whether the computing device is idle or busy in the first time period. The method outputs an indication of the computing device being idle responsive to a classification that the computing device is idle.