Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive a first multiple telemetry data from multiple information handling systems (IHSs); determine first multiple performance health scores respectively associated with the IHSs; determine first multiple availability scores respectively associated with the IHSs; determine first multiple information handling system (IHS) update churn scores respectively associated with the IHSs; determine, via a machine learning process, a second distribution of the IHSs to the multiple IHS groups based at least on the first multiple IHS and performance health scores, the first multiple availability scores, and the first multiple IHS churn scores; and provide a first software update to IHSs of each IHS group.

Abstract: Methods, apparatus, and processor-readable storage media for automated issue detection and remediation across multiple user systems using healing-as-a-service techniques are provided herein. An example computer-implemented method includes obtaining system configuration data from at least a portion of multiple user systems within a network; obtaining an alert pertaining to an issue attributed to a first of the user systems; training a machine learning model related to user system issue detection using at least a portion of the system configuration data and data related to the alert; determining user system configuration adjustments related to remedying at least a portion of the issue, by processing the data related to the alert using the trained machine learning model; automatically performing the user system configuration adjustments in connection with the first user system; and sharing, using at least one healing-as-a-service component, the trained machine learning model with the user systems in the network.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive a first multiple telemetry data from multiple information handling systems (IHSs); determine first multiple performance health scores respectively associated with the IHSs; determine first multiple availability scores respectively associated with the IHSs; determine first multiple information handling system (IHS) update churn scores respectively associated with the IHSs; determine, via a machine learning process, a second distribution of the IHSs to the multiple IHS groups based at least on the first multiple IHS and performance health scores, the first multiple availability scores, and the first multiple IHS churn scores; and provide a first software update to IHSs of each IHS group.

Abstract: Methods, apparatus, and processor-readable storage media for automated issue detection and remediation across multiple user systems using healing-as-a-service techniques are provided herein. An example computer-implemented method includes obtaining system configuration data from at least a portion of multiple user systems within a network; obtaining an alert pertaining to an issue attributed to a first of the user systems; training a machine learning model related to user system issue detection using at least a portion of the system configuration data and data related to the alert; determining user system configuration adjustments related to remedying at least a portion of the issue, by processing the data related to the alert using the trained machine learning model; automatically performing the user system configuration adjustments in connection with the first user system; and sharing, using at least one healing-as-a-service component, the trained machine learning model with the user systems in the network.

Abstract: According to one illustrative, non-limiting embodiment, an IHS may include computer-executable instructions for receiving individual-specific raw data instances associated with an individual, and other raw data instances that are not associated with the individual to perform a native artificial intelligence (AI) process to generate one or more native features according to the received other raw data instances and the individual-specific raw data instances, and a native AI model from the one or more native features. The instructions also perform a differential AI process to generate one or more differential features according to the received other raw data instances, and a differential model from the one or more differential features.