Abstract: Method, apparatus and computer program product for performing a cross-model predictive inference to generate a cross-model predictive output for a plurality of predictive inputs using a plurality of predictive models. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, obtain a model selection probability distribution which defines, for each predictive model, a respective selection probability score; obtain, for each predictive model, respective cross-model normalization data; for each predictive input, determine a cross-model predictive score; and determine, based on each determined cross-model predictive score, the cross-model predictive output.

Abstract: Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data.

Abstract: A method, apparatus, and computer program, each configured for determining a travel propensity, the travel propensity being a distance a customer is willing to travel to redeem a renderable data object, are provided An exemplary method comprises classifying each of a plurality of geographic regions as one geographic region type of a plurality of geographic region types, mapping each of a plurality of redemption locations to a geographic region type, calculating, for a first geographic region type, a first distance that accounts for a predefined number of redemptions, determining a number of merchants having redemption locations within the first distance, calculating a second distance, from a center of a geographic region of the second geographic region type, necessary to include the number of merchants having redemption locations within the first distance, and assigning the second distance as the second geographic region type travel propensity.

Abstract: Method, apparatus and computer program product for performing a cross-model predictive inference to generate a cross-model predictive output for a plurality of predictive inputs using a plurality of predictive models. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, obtain a model selection probability distribution which defines, for each predictive model, a respective selection probability score; obtain, for each predictive model, respective cross-model normalization data; for each predictive input, determine a cross-model predictive score; and determine, based on each determined cross-model predictive score, the cross-model predictive output.

Abstract: A method, apparatus, and computer program, each configured for determining a travel propensity, the travel propensity being a distance a customer is willing to travel to redeem a renderable data object, are provided An exemplary method comprises classifying each of a plurality of geographic regions as one geographic region type of a plurality of geographic region types, mapping each of a plurality of redemption locations to a geographic region type, calculating, for a first geographic region type, a first distance that accounts for a predefined number of redemptions, determining a number of merchants having redemption locations within the first distance, calculating a second distance, from a center of a geographic region of the second geographic region type, necessary to include the number of merchants having redemption locations within the first distance, and assigning the second distance as the second geographic region type travel propensity.

Abstract: A method, apparatus, and computer program, each configured for gauging the health or quality of available supply, are provided. An exemplary method for generating a supply index indicative of a quality or health of supply by calculating and aggregating a supply metric calculated from a perspective of each of a plurality of individual customers to identify those locations where an increase in supply would most positively impact value to customers comprises identifying a location of a user, identifying at least a portion of each of one or more renderable data objects available to a user located at the location, applying a first weight to each of the portion of each of the one or more renderable data objects, calculating a total of each of the weights, applying a coverage cap to determine a capped total, and generating a supply index comprised of aggregated capped totals.

Abstract: A method, apparatus, and computer program, each configured for gauging the health or quality of available supply, are provided. An exemplary method for generating a supply index indicative of a quality or health of supply by calculating and aggregating a supply metric calculated from a perspective of each of a plurality of individual customers to identify those locations where an increase in supply would most positively impact value to customers comprises identifying a location of a user, identifying at least a portion of each of one or more renderable data objects available to a user located at the location, applying a first weight to each of the portion of each of the one or more renderable data objects, calculating a total of each of the weights, applying a coverage cap to determine a capped total, and generating a supply index comprised of aggregated capped totals.

Abstract: Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data.

Abstract: Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data.