Abstract: Apparatuses, systems, program products, and methods are disclosed for interpretability-based machine learning adjustment during production. An apparatus includes a first results module that is configured to receive a first set of inference results of a first machine learning algorithm during inference of a production data set. An apparatus includes a second results module that is configured to receive a second set of inference results of a second machine learning algorithm during inference of a production data set. An apparatus includes an action module that is configured to trigger one or more actions that are related to a first machine learning algorithm in response to a comparison of first and second sets of inference results not satisfying explainability criteria.

Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: A high speed computer that permits the partitioning of a single computer program into smaller concurrent processes running in different parallel processors. The program execution time is divided into synchronous phases, each of which may require a shared memory to be configured in a distinct way. At the end of each execution phase, the processors are resynchronized such that the composite system will be in a known state at a known point in time. The computer makes efficient use of hardware such that n processors can solve a problem almost n times as fast as a single processor.