Dynamic multi-model monitoring and validation for artificial intelligence models

A computer-implemented method for multi-model monitoring and validation of an artificial intelligence model, the method comprising:

• obtaining an artifact set generated using a first AI model set within a multi-model superstructure, wherein the multi-model superstructure includes:
• (i) the first AI model set and (ii) a second AI model set to test the first AI model set;
• assigning, by the multi-model superstructure, the artifact set of the first AI model set to one or more AI models of the second AI model set,
• wherein the one or more AI models of the second AI model set are determined based on a predefined schedule;
• inputting one or more artifacts of the artifact set into the second AI model set to use at least one hardware processor to output an indication of satisfaction of the one or more artifacts with an assessment metric set of an assessment set;
• and
• generating, by the second AI model set, an action set to modify one or more AI models of the first AI model set using the indication of satisfaction of the one or more artifacts with the assessment metric set of the assessment set.

A computer-implemented method for multi-model monitoring and validation of an artificial intelligence model, the method comprising:

• obtaining an artifact set generated using a first AI model set within a multi-model superstructure, wherein the multi-model superstructure includes:
• (i) the first AI model set and (ii) a second AI model set to test the first AI model set;
• assigning, by the multi-model superstructure, the artifact set of the first AI model set to one or more AI models of the second AI model set;
• inputting one or more artifacts of the artifact set into the second AI model set to use at least one hardware processor to output an indication of satisfaction of the one or more artifacts with an assessment metric set of an assessment set,
• wherein the second AI model set is configured to determine whether a particular assessment metric value fails to satisfy a particular threshold metric value using a majority vote between the one or more AI models of the second AI model set;
• and
• generating, by the second AI model set, an action set to modify one or more AI models of the first AI model set using the indication of satisfaction of the one or more artifacts with the assessment metric set of the assessment set.

A computer-implemented method for multi-model monitoring and validation of an artificial intelligence model, the method comprising:

• obtaining an artifact set generated using a first AI model set within a multi-model superstructure, wherein the multi-model superstructure includes:
• (i) the first AI model set and (ii) a second AI model set to test the first AI model set,
• wherein the second AI model set includes a domain-specific AI model set;
• assigning, by the multi-model superstructure, the artifact set of the first AI model set to one or more AI models of the second AI model set,
• wherein the artifact set is routed to the one or more AI models of the second AI model set trained on data sharing a common domain with one or more artifacts of the artifact set;
• inputting one or more artifacts of the artifact set into the second AI model set to use at least one hardware processor to output an indication of satisfaction of the one or more artifacts with an assessment metric set of an assessment set;
• and
• generating, by the second AI model set, an action set to modify one or more AI models of the first AI model set using the indication of satisfaction of the one or more artifacts with the assessment metric set of the assessment set.