Abstract: Systems, apparatuses, methods, and computer program products are disclosed for improving efficiency and performance of a modeling system. An example method for automatically adjusting a machine learning model in response to a decline in performance of the machine learning model caused by a change in exogenous context includes determining a set of context-relevant variables from a silent variable set that are relevant to the change in exogenous context. The example method also includes training the machine learning model by activating a fewest number of context-relevant variables from the set of context-relevant variables that exhibit a mitigation of the decline in performance of the machine learning model. The example method also includes generating, after training the machine learning model, a first predictive output of the machine learning model that indicates an outcome projected to occur as a result of the change in exogenous context.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for improving efficiency and performance of a modeling system. An example method includes receiving, by communications circuitry, a silent variable set. The example method also includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes adjusting, by model adjustment circuitry, the modeling system based on the silent variable set.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for mitigating deterioration of modeling system performance. An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes determining, by context analysis circuitry and in response to detecting the occurrence of the triggering condition, a destination modeling solution based on a regime strategy for the modeling system. The example method also includes switching, by regime deployment circuitry and based on the regime strategy, from a source modeling solution to a destination modeling solution.

Abstract: An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes scheduling, by context analysis circuitry and based on the occurrence of the triggering condition, retraining of a model. The example method also includes generating, by data grafting circuitry and in response to scheduling the retraining of the model, a context-relevant training data set based on a target context vector. The example method also includes retraining, by model training circuitry, the model using the context-relevant training data set to mitigate deterioration of performance of the model.

Abstract: An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes scheduling, by context analysis circuitry and based on the occurrence of the triggering condition, retraining of a model. The example method also includes generating, by data grafting circuitry and in response to scheduling the retraining of the model, a context-relevant training data set based on a target context vector. The example method also includes retraining, by model training circuitry, the model using the context-relevant training data set to mitigate deterioration of performance of the model.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for mitigating deterioration of modeling system performance. An example method includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes determining, by context analysis circuitry and in response to detecting the occurrence of the triggering condition, a destination modeling solution based on a regime strategy for the modeling system. The example method also includes switching, by regime deployment circuitry and based on the regime strategy, from a source modeling solution to a destination modeling solution.