CONTROLLING MACHINE-LEARNING MODELS IN REALTIME SYSTEMS

原文（中国語）

A method for controlling machine-learning models in real-time or near real-time systems is provided. The method includes accessing a set of sensor data captured from sensors configured to detect operational parameters associated with an operation of a real-time or near real-time system, and further inputting the set of sensor data into an ensemble machine-learning model trained to generate a prediction of features of detected operational parameters based on the set of sensor data. The ensemble machine-learning model includes a plurality of machine-learning models trained to generate the prediction of the features. The method further includes outputting, by the ensemble machine-learning model, the prediction of the features, generating, based on the prediction of the features, an explainability output associated with each of the plurality of machine-learning models, and further generating, based on the explainability output, one or more relative commonality scores for each of the plurality of machine-learning models.