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 data quality detection and compensation for machine learning. A quality analysis module electronically identifies one or more data quality issues in machine learning training data. A corrective action module modifies training data by performing one or more corrective actions in response to one or more data quality issues. A predictive analytics module creates a machine learning model that includes one or more learned functions based on modified training data.

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 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: 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 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.