AI customized MLOps framework in a multi-tenant cloud environment

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions that when executed:

• automatically select one of at least one subscription machine learning (ML) models in a model registry based on a tenant configuration profile of a tenant, responsive to receiving a request for the subscription ML model, wherein the tenant configuration profile includes information associated with cloud service needs of the tenant;
• deploy the automatically selected subscription ML model to the tenant as a currently operating ML model;
• monitor usage of the currently operating ML model at a pre-determined refresh frequency using a dynamic configuration machine learning model, the pre-determined refresh frequency based at least on the tenant configuration profile and a configuration profile category of the currently operating ML model;
• determine whether the currently operating ML model exceeds a pre-determined accuracy threshold;
• and
• automatically deploy a second subscription ML model from the model registry to the tenant in place of the currently operating ML model and becoming the currently operating ML model in response to the pre-determined accuracy threshold not being exceeded.

A system comprising:

• a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions that when executed:
• automatically select one of at least one subscription machine learning (ML) models in a model registry based on a tenant configuration profile of a tenant, responsive to receiving a request for the subscription ML model, wherein the tenant configuration profile includes information associated with cloud service needs of the tenant;
• deploy the automatically selected subscription ML model to the tenant as a currently operating ML model;
• monitor usage of the currently operating ML model at a pre-determined refresh frequency using a dynamic configuration machine learning model, the pre-determined refresh frequency based at least on the tenant configuration profile and a configuration profile category of the currently operating ML model;
• determine whether the currently operating ML model exceeds a pre-determined accuracy threshold;
• and
• automatically deploy a second subscription ML model from the model registry to the tenant in place of the currently operating ML model and becoming the currently operating ML model in response to the pre-determined accuracy threshold not being exceeded.