Method and apparatus for monitoring machine learning models

A non-transitory computer readable medium comprising a computer program product comprising computer executable instructions stored in a tangible medium that, when executed by a computer and/or an artificial neural net, ANN, cause the computer to and/or the ANN to determine a quality measure of a first control model for monitoring or controlling an industrial process, wherein the first control model comprises a first input, a first input layer, a plurality of first hidden layers, a first output layer, and a first output, the determining of the quality measure comprising:

• training the first control model by utilizing a first set of input data as first input, resulting in a trained first control model;
• copying the trained first control model to a second control model, wherein the second control model comprises a second input, a second input layer, a plurality of second hidden layers, a second output layer, and a second output that is configured to output the quality measure of the first control model;
• wherein, after copying, the second input layer is identical to the first input layer, the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer;
• freezing at least parts of the plurality of second hidden layers;
• training the second control model by utilizing the first set of input data as a second input, resulting in a trained second control model;
• and
• running the trained second control model by utilizing a second set of input data as the second input, wherein the second output outputs the quality measure of the first control model.