Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Abstract: Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.

Abstract: Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.

Abstract: Embodiments relate to a system, program product, and method for training a contrastive neural network (CNN) in an active learning environment. A neural network is pre-trained with labeled data of a historical (first) dataset. The CNN is trained for a new (second) dataset by applying the new dataset and contrasting the new dataset against the historical dataset to extract novel patterns. Weights of a knowledge operator from the pre-trained neural network are borrowed. Features novel to the new dataset are learned, including updating weights of the knowledge operator. The borrowed knowledge operator weights are combined with the updated knowledge operator weights. The CNN is leveraged to predict one or more labels for the new dataset as output data.

Abstract: Technology for applying artificial intelligence to decide when to, and/or when not to, send a consumer of a computer system a communication recommending that the computer system be revised to include a more recent version of at least one of the following: a hardware component (for example, microprocessor(s)) and/or a software component (for example, an updated version of an app). The computer system, that is subject to modernization, may be owned outright by the consumer, or it may be purchased as a service (for example, infrastructure as a service, software as a service, package of cloud services). Some embodiments focus on modernization recommendations specifically tailored to cloud orchestration software that deploys containers.

Abstract: A computer system, non-transitory computer storage medium, and a computer-implemented method of assessing technical risk using visual pattern recognition in an Information Technology (IT) Service Management System. A data visualization engine and a time series generation engine receive the operational data, respectively. A first representation of the data is generated by the data visualization engine, and a second representation of the data is generated by the time series generation engine. Anomaly patterns are identified by a pattern recognition engine configured to perform feature extraction and data transformation. An ensembler is configured to accept the outputs from two AI anomaly engines and make a final decision of whether anomaly patterns are captured. Risk scores based on the identified anomaly patterns are output by a pattern recognition engine to an automated management system. The anomalies includes information regarding vulnerabilities of devices or components of the IT Service Management System.

Abstract: Aspects of the invention include receiving, using a processor, a plurality of values of a performance indicator. A statistical analysis of the plurality of values of the performance indicator is performed, using the processor, to detect an anomaly pattern in the plurality of values of the performance indicator. A warning message about the detected anomaly pattern is sent to an alert recipient that is selected by a machine learning model trained to identify alert recipients based at least in part on detected anomaly patterns. Feedback about the warning message is received from the alert recipient. The feedback includes an interest of the alert recipient in receiving warning messages about the detected anomaly pattern. The machine learning model is updated based at least in part on the feedback.

Abstract: An approach to optimized migration of user assets to the cloud using artificial intelligence is presented. This approach may user input and artificial intelligence trained with historical knowledge to generate rules. Migration models may be generated from the rules. A user may verify the migration models were successful. A task portfolio may be generated from the verified wave migration models. Runbook applications may be generated from the task portfolio and the migration may be executed using the runbooks.

Abstract: Embodiments relate to a system, program product, and method for training a contrastive neural network (CNN) in an active learning environment. A neural network is pre-trained with labeled data of a historical dataset. The CNN is trained for the new dataset by applying the new dataset and contrasting the new dataset against the historical dataset to extract novel patterns. Features novel to the new dataset are learned, including updating weights of the knowledge operator. The borrowed knowledge operator weights are combined with the updated knowledge operator weights. The CNN is leveraged to predict one or more labels for the new dataset as output data.

Abstract: A computer system, non-transitory computer storage medium, and a computer-implemented method of assessing technical risk using visual pattern recognition in an Information Technology (IT) Service Management System. A data visualization engine and a time series generation engine receive the operational data, respectively. A first representation of the data is generated by the data visualization engine, and a second representation of the data is generated by the time series generation engine. Anomaly patterns are identified by a pattern recognition engine configured to perform feature extraction and data transformation. An ensembler is configured to accept the outputs from two AI anomaly engines and make a final decision of whether anomaly patterns are captured. Risk scores based on the identified anomaly patterns are output by a pattern recognition engine to an automated management system. The anomalies includes information regarding vulnerabilities of devices or components of the IT Service Management System.

Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Abstract: Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.

Abstract: Aspects of the invention include receiving, using a processor, a plurality of values of a performance indicator. A statistical analysis of the plurality of values of the performance indicator is performed, using the processor, to detect an anomaly pattern in the plurality of values of the performance indicator. A warning message about the detected anomaly pattern is sent to an alert recipient that is selected by a machine learning model trained to identify alert recipients based at least in part on detected anomaly patterns. Feedback about the warning message is received from the alert recipient. The feedback includes an interest of the alert recipient in receiving warning messages about the detected anomaly pattern. The machine learning model is updated based at least in part on the feedback.