Selective machine learning model deployment using predicted adjustments to an item grouping

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured to implement the following steps:
• accessing at least one data structure, wherein the at least one data structure corresponds to an item grouping of one or more items and comprises a plurality of feature values related to the item grouping;
• applying at least portions of the data structure to (i) at least one first machine learning (ML)-based score prediction model and (ii) at least one second ML-based score prediction model to obtain data characterizing respective predicted scores for the item grouping;
• applying at least portions of the respective predicted scores for the item grouping to at least one ML-based item grouping adjustment prediction model to obtain respective predicted adjustments to one or more parameters of the item grouping;
• applying at least portions of the respective predicted adjustments to at least one processor-based selective model deployment system to obtain data characterizing a deployment decision, wherein the at least one processor-based selective model deployment system evaluates a performance of (i) the at least one first ML-based score prediction model and (ii) the at least one second ML-based score prediction model, based at least in part on the respective predicted adjustments to the one or more parameters of the item grouping, to determine the deployment decision;
• and
• initiating one or more processing steps based at least in part on the deployment decision.

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:

• accessing at least one data structure, wherein the at least one data structure corresponds to an item grouping of one or more items and comprises a plurality of feature values related to the item grouping;
• applying at least portions of the data structure to (i) at least one first machine learning (ML)-based score prediction model and (ii) at least one second ML-based score prediction model to obtain data characterizing respective predicted scores for the item grouping;
• applying at least portions of the respective predicted scores for the item grouping to at least one ML-based item grouping adjustment prediction model to obtain respective predicted adjustments to one or more parameters of the item grouping;
• applying at least portions of the respective predicted adjustments to at least one processor-based selective model deployment system to obtain data characterizing a deployment decision, wherein the at least one processor-based selective model deployment system evaluates a performance of (i) the at least one first ML-based score prediction model and (ii) the at least one second ML-based score prediction model, based at least in part on the respective predicted adjustments to the one or more parameters of the item grouping, to determine the deployment decision;
• and
• initiating one or more processing steps based at least in part on the deployment decision.