Real time detection, prediction and remediation of machine learning model drift in asset hierachy based on time-series data

Abstract

Model drift management of one or more machine learning models deployed across one or more physical systems, including executing a first process configured to detect model drift occurring on the one or more deployed machine learning models in real time, the first process configured to intake time series sensor data of one or more physical systems and one or more labels associated with the time series sensor data to output detected model drift detected from the one or more deployed machine learning models; and executing a second process configured to predict model drift from the one or more deployed machine learning models, the second process configured to intake the output model drifts from the first machine learning model and the time series sensor data to output predicted model drift of the one or more deployed machine learning models, wherein the second process is another machine learning model.

Dependencies are inferred from the claim text.