Andrew K Nam

20210224425, Jul 22, 2021

Jan 20, 2021

17/153258

- Dublin, IE
Andrew Hoonsik Nam - San Francisco CA, US
Matthew Kujawinski - San Jose CA, US
Teresa Sheausan Tung - Tustin CA, US

G06F 21/64
G06N 20/20

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.

20210224696, Jul 22, 2021

Jan 20, 2021

17/153237

- Dublin, IE
Andrew Hoonsik Nam - San Francisco CA, US
Teresa Sheausan Tung - Tustin CA, US

G06N 20/20
G06N 5/00
G06N 5/04

Complex computer system architectures are described for detecting a concept drift of a machine learning model in a production environment, for adaptive optimization of the concept drift detection, for extracting embedded features associated with the concept drift using a shadow learner, and for adaptive adjustment of the machine learning model in production to mitigate the effect of predictive performance drop due to the concept drift.