Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support detection and mitigation of defects occurring in outputs of a process. A modelling engine creates and trains models to identify defects occurring in outputs of a process or system. A set of causation rules may be created and trained to identify causes of different defects identifiable via the models and a set of control rules may be created and trained to generate control data that may mitigate the causes of identified defects. A process monitoring device incorporating the trained models and rules may then be used to monitor the process and detect defects using the models. Once a defect is detected, the rules may be applied to determine the cause of the defect and then control data may be generated to modify the process such that further occurrences of the defect are reduced or eliminated.

Abstract: Embodiments provide for prediction and mitigation of network faults. Information associated network nodes may be compiled and used to generate network analytical records (NARs). A first model may be executed against the NARs to predict faults associated with one or more nodes of the network. Tickets are generated for predicted faults and stored in a ticket database. The tickets may be analyzed to predict executable actions to mitigate the faults associated with each ticket. To analyze the tickets, ticket data may be compiled and used to generate ticket analytical records (TARs). A second model may be executed against the TARs predict actions to resolve the predicted faults. The predicted actions may be executed to mitigate the impact that the faults have on the network, which may include preventing the faults entirely (e.g., via preventative maintenance) or minimizing the impact of the faults via use of the predicted actions.