Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of data logs; generating a data model using analyzed time series data from the identified data logs; detecting anomalies within the generated data model; constructing a causal graph using the detected anomalies and retrieved domain knowledge; computing a severity value for the detected anomalies with the constructed causal graph; assigning the detected anomaly to a classification based on a function vector, wherein the computed severity value is a function vector; and automatically modifying a function of a computing device based on the function vector of the assigned, detected anomaly, wherein a modification addresses the detected anomaly located at a center of the constructed casual graph.

Abstract: Methods, systems, and computer program products for automatically detecting periods of normal activity by analyzing observability data in IT operations environments are provided herein. A computer-implemented method includes obtaining multiple types of data related to one or more artificial intelligence-related information technology operations; modelling at least a portion of the obtained data as time series data; automatically identifying, from the time series data, one or more time periods associated with one or more given levels of data activity; and performing one or more automated actions, in at least one artificial intelligence-related information technology operations environment, based at least in part on the data corresponding to the one or more identified time periods.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of data logs; generating a data model using analyzed time series data from the identified data logs; detecting anomalies within the generated data model; constructing a causal graph using the detected anomalies and retrieved domain knowledge; computing a severity value for the detected anomalies with the constructed causal graph; assigning the detected anomaly to a classification based on a function vector, wherein the computed severity value is a function vector; and automatically modifying a function of a computing device based on the function vector of the assigned, detected anomaly, wherein a modification addresses the detected anomaly located at a center of the constructed casual graph.