Model lifecycle management for closed-loop processes within industrial automation environments

A non-transitory computer-readable medium having stored thereon instructions for managing model lifecycles in an industrial automation environment, wherein the instructions, in response to execution, cause a system comprising a processor to perform operations, the operations comprising:

• running a closed-loop industrial process, in the industrial automation environment, with a control program, wherein the control program comprises a first machine learning model;
• comparing, by one or more lifecycle management machine learning models, outcome data predicted by the first machine learning model and actual outcome data of the closed-loop industrial process;
• upon detecting that a prediction error between the outcome data predicted by the first machine learning model and the actual outcome data exceeds a degradation threshold, identifying, by the one or more lifecycle management machine learning models, an error reduction strategy, wherein the error reduction strategy includes replacing the first machine learning model in the control program with a second machine learning model;
• and
• replacing the first machine learning model in the control program with the second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data of the closed-loop industrial process.

A method for managing model lifecycles comprising:

• running, by a system comprising a processor, a closed-loop industrial process, in an industrial automation environment, with a control program, wherein the control program comprises a first machine learning model;
• comparing, by one or more lifecycle management machine learning models on the system, outcome data predicted by the first machine learning model and actual outcome data of the closed-loop industrial process;
• upon detecting that a prediction error between the outcome data predicted by the first machine learning model and the actual outcome data exceeds a degradation threshold, identifying, by the one or more lifecycle management machine learning models on the system, an error reduction strategy, wherein the error reduction strategy includes replacing the first machine learning model in the control program with a second machine learning model;
• and
• replacing, by the system, the first machine learning model in the control program with the second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data of the closed-loop industrial process.