Abstract: Embodiments of the present disclosure provide for providing performance optimization predictions related to an entity dataset. Such embodiments may include selecting a candidate machine learning model from a plurality of candidate machine learning models based at least in part on (i) a data profile for an entity dataset associated with an entity identifier and (ii) a predefined model profile associated with the candidate machine learning model. Such embodiments may additionally or alternatively include generating a candidate feature set by modifying a predefined feature set for the candidate machine learning model based at least in part on the data profile associated with the entity identifier. Such embodiments may additionally or alternatively include generating one or more performance optimization data objects by applying the candidate machine learning model to the candidate feature set based at least in part on an objective rules set for the entity identifier.

Abstract: An example apparatus, computer-implemented method, and computer program product for autonomously training a content generation framework using an autonomously-generated dynamic framework feature set is provided. An example apparatus may include instructions configured to cause the apparatus to receive a user experience content dataset having target client characteristics related to a plurality of target clients. The apparatus may be further configured to generate exploratory feature sets including target client characteristics, and generate a normalized exploratory feature set score based on one or more content generation objectives. The apparatus further configured to generate a dynamic framework feature set comprised of selected features of the user experience content dataset, and train a content generation learning model based on the dynamic framework feature set to determine content data objects customized for the target clients.

Abstract: Various embodiments are directed to an example apparatus, computer-implemented method, and computer program product for rapid machine learning in a data-constrained environment. Such embodiments may include using a decision space generation model to generate candidate content data objects based on content generation objectives. Such embodiments may further include generating a first plurality of rated content data objects for a first target client based on a first experimental classification group and generating a second plurality of rated content data objects for a second target client based on a second experimental classification group. Such embodiments may further generate, based on a learning model, the first experimental classification group, and the second experimental classification group, a custom output content set including one or more of the first plurality of rated content data objects and one or more of the second plurality of rated content data objects.

Abstract: Embodiments of the present disclosure provide for providing performance optimization predictions related to an entity dataset. Such embodiments may include selecting a candidate machine learning model from a plurality of candidate machine learning models based at least in part on (i) a data profile for an entity dataset associated with an entity identifier and (ii) a predefined model profile associated with the candidate machine learning model. Such embodiments may additionally or alternatively include generating a candidate feature set by modifying a predefined feature set for the candidate machine learning model based at least in part on the data profile associated with the entity identifier. Such embodiments may additionally or alternatively include generating one or more performance optimization data objects by applying the candidate machine learning model to the candidate feature set based at least in part on an objective rules set for the entity identifier.

Abstract: Embodiments of the present disclosure provide for providing performance optimization predictions related to an entity dataset. Such embodiments may include selecting a candidate machine learning model from a plurality of candidate machine learning models based at least in part on (i) a data profile for an entity dataset associated with an entity identifier and (ii) a predefined model profile associated with the candidate machine learning model. Such embodiments may additionally or alternatively include generating a candidate feature set by modifying a predefined feature set for the candidate machine learning model based at least in part on the data profile associated with the entity identifier. Such embodiments may additionally or alternatively include generating one or more performance optimization data objects by applying the candidate machine learning model to the candidate feature set based at least in part on an objective rules set for the entity identifier.

Abstract: Various embodiments are directed to an example apparatus, computer-implemented method, and computer program product for rapid machine learning in a data-constrained environment. Such embodiments may include using a decision space generation model to generate candidate content data objects based on content generation objectives. Such embodiments may further include generating a first plurality of rated content data objects for a first target client based on a first experimental classification group and generating a second plurality of rated content data objects for a second target client based on a second experimental classification group. Such embodiments may further generate, based on a learning model, the first experimental classification group, and the second experimental classification group, a custom output content set including one or more of the first plurality of rated content data objects and one or more of the second plurality of rated content data objects.

Abstract: Various embodiments are directed to an example apparatus, computer-implemented method, and computer program product for rapid machine learning in a data-constrained environment. Such embodiments may include using a decision space generation model to generate candidate content data objects based on content generation objectives. Such embodiments may further include generating a first plurality of rated content data objects for a first target client based on a first experimental classification group and generating a second plurality of rated content data objects for a second target client based on a second experimental classification group. Such embodiments may further generate, based on a learning model, the first experimental classification group, and the second experimental classification group, a custom output content set including one or more of the first plurality of rated content data objects and one or more of the second plurality of rated content data objects.