Abstract: Apparatuses, systems, methods, and computer program products are disclosed for a machine learning factory. A receiver module is configured to receive electronically transmitted training data. A function generator module is configured to generate executable program code for a plurality of learned functions from a plurality of different machine learning classes based on training data. A function evaluator module is configured to perform a machine learning evaluation of a plurality of learned functions using test data and configured to maintain evaluation metadata in one or more non-transitory computer readable storage media.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for machine learning in a data management product. The apparatus includes an input module, a learned function module, and a results module. The input module is configured to receive an analysis request for the data management product. The learned function module is configured to execute one or more machine learning ensembles to predict one or more unknown values for the data management product. The result module is configured to provide native access, within the data management product, to the one or more unknown values.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for drift detection and correction for predictive analytics. A prediction module applies a model to workload data to produce one or more predictive results. Workload data may include one or more records. A model may include one or more learned functions based on training data. A drift detection module detects a drift phenomenon relating to one or more predictive results. A predict-time fix module may modify at least one predictive result in response to a drift phenomenon.

Abstract: Apparatuses, systems, methods, and computer program products are presented for performing data analytics using machine learning. An unsupervised learning module is configured to assemble an unstructured data set into multiple versions of an organized data set. A supervised learning module is configured to generate one or more machine learning ensembles based on each version of multiple versions of an organized data set and to determine which machine learning ensemble exhibits a highest predictive performance.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for machine learning results. An input module may receive user input identifying a value for a machine learning parameter. A display module may display one or more machine learning results for the identified machine learning parameter in response to the input module receiving the user input. An update module may dynamically update the displayed one or more machine learning results in response to the input module receiving additional user input identifying an additional value for the machine learning parameter. A pre-compute module may predetermine permutations of the machine learning results prior to the input module receiving the user input.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for a predictive analytics factory. A function generator module is configured to determine a plurality of learned functions based on training data without prior knowledge regarding suitability of the generated learned functions for the training data. A function evaluator module is configured to perform an evaluation of the plurality of learned functions using test data and to maintain evaluation metadata for the plurality of learned functions. A predictive compiler module is configured to form a predictive ensemble comprising a subset of multiple learned functions from the plurality of learned functions.

Abstract: Apparatuses, systems, methods, and computer program products are presented for performing data analytics using machine learning. An unsupervised learning module is configured to assemble an unstructured data set into multiple versions of an organized data set. A supervised learning module is configured to generate one or more machine learning ensembles based on each version of multiple versions of an organized data set and to determine which machine learning ensemble exhibits a highest predictive performance.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for a machine learning generated action plan. A machine learning module is configured to process different instances of data using machine learning to produce one or more results. The different instances of data may comprise different values for one or more actionable features. A recommended action module is configured to select one or more recommended actions for achieving a goal associated with the machine learning. The recommended action module may select the one or more recommended actions based on the one or more results. An action plan interface module is configured to provide an action plan associated with the one or more recommended actions.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for machine learning results. An input module may receive user input identifying a value for a machine learning parameter. A display module may display one or more machine learning results for the identified machine learning parameter in response to the input module receiving the user input. An update module may dynamically update the displayed one or more machine learning results in response to the input module receiving additional user input identifying an additional value for the machine learning parameter. A pre-compute module may predetermine permutations of the machine learning results prior to the input module receiving the user input.

Abstract: An apparatus, system, method, and computer program product are disclosed for a predictive analytics factory. A receiver module is configured to receive training data. A function generator module is configured to determine a plurality of learned functions from multiple classes based on the training data. A predictive compiler module is configured to form a predictive ensemble comprising a subset of learned functions from the plurality of learned functions. The subset of learned functions is from at least two of the multiple classes.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for activity based incentives. A tracking module may be configured to monitor a user's participation in a physical activity. A data module may be configured to collect activity data in response to monitoring the user's participation in the physical activity. An incentive module may be configured to present one or more retail incentives to the user based on the activity data.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for website interaction. An input module is configured to receive information from multiple sources. The information may be associated with a user of a website. A machine learning module is configured to apply machine learning to the information to produce a machine learning result. A website adaptation module is configured to adapt the website for the user in real-time based on the machine learning result.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for determining student engagement. A method includes receiving data collected from interactions of a plurality of students with an electronic learning system. A method includes identifying a plurality of archetypal learning patterns in received data using machine learning. A method may also include associating a student with at least one identified archetypal learning patterns using machine learning.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for machine learning in a data management product. The apparatus includes an input module, a learned function module, and a results module. The input module is configured to receive an analysis request for the data management product. The learned function module is configured to execute one or more machine learning ensembles to predict one or more unknown values for the data management product. The result module is configured to provide native access, within the data management product, to the one or more unknown values.

Abstract: An apparatus, system, method, and computer program product are disclosed for systems management. The method includes receiving user information and systems management data as machine learning inputs. The user information labels a state of one or more computing resources. The method includes recognizing a pattern, using machine learning, in the systems management data. The method includes modifying a configuration of a systems management system based on the labeled state and the recognized pattern.

Abstract: An apparatus, system, method, and computer program product are disclosed for a predictive analytics factory. A receiver module is configured to receive training data. A function generator module is configured to determine a plurality of learned functions from multiple classes based on the training data. A predictive compiler module is configured to form a predictive ensemble comprising a subset of learned functions from the plurality of learned functions. The subset of learned functions is from at least two of the multiple classes.