ML model drift detection using modified GAN

Abstract

Discussed herein are devices, systems, and methods for machine learning (ML) model drift detection. A method can include receiving machine learning (ML) data defining a number of layers of neurons, a number of neurons per each layer, and weights for each neuron of a deployed ML model, operating the deployed ML model in a modified generative adversarial network (GAN) architecture, while operating the deployed ML model, recording output of a hidden layer of the deployed ML model, determining a metric of the output, and re-deploying the deployed ML model and monitoring whether the re-deployed ML model is suffering from ML model drift based on the metric.

CPC Classifications