Abstract: Present disclosure relates to management of artificial intelligence systems by identifying root cause of reduced performance and/or failure in computing systems, and particularly relates to systems and methods for detecting a drift in supervised and unsupervised machine learning (ML) models. The system retrieves current dataset corresponding to output of supervised ML models and unsupervised ML models. Further, the system segregates the current dataset based on requirement of a drift detection model and applies a plurality of drift detection models to the segregated dataset to generate predictive results corresponding to the current dataset. Furthermore, the system determines errors in predictive results by comparing predictive results to reference values associated with current dataset. Additionally, the system detects the drift in supervised ML models and unsupervised ML models based on determined errors being above a threshold value.

Abstract: Present disclosure relates to management of artificial intelligence systems by identifying root cause of reduced performance and/or failure in computing systems, and particularly relates to systems and methods for detecting a drift in supervised and unsupervised machine learning (ML) models. The system retrieves current dataset corresponding to output of supervised ML models and unsupervised ML models. Further, the system segregates the current dataset based on requirement of a drift detection model and applies a plurality of drift detection models to the segregated dataset to generate predictive results corresponding to the current dataset. Furthermore, the system determines errors in predictive results by comparing predictive results to reference values associated with current dataset. Additionally, the system detects the drift in supervised ML models and unsupervised ML models based on determined errors being above a threshold value.