Machine learning model robustness against adversarial attacks in production

Abstract

This disclosure is directed to a generalizable machine learning model production environment and system with a defense mechanism that facilitates safe execution of machine learning models in production by effectively detecting potential known and new adversarial attacks. The disclosed exemplary systems and architectures gather data from the online execution of the machine learning models and communicate with an on-demand pipelines for further inspection and/or correction of vulnerabilities in the production machine learning model to the detected attacks. These systems and architectures provide an automatable process for continuous monitoring of model performance and correction of the production machine learning model to guard against current and future adversarial attacks.