Abstract: Systems and methods for deploying instances of Artificial Intelligence (AI) models having different levels of complexity in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS), may include a heterogeneous computing platform comprising a plurality of devices and a memory having a plurality of sets of firmware instructions that, upon execution by a respective device, enable the respective device to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: select an instance of an AI model among a plurality of instances of the AI model based, at least in part, upon context or telemetry data received from at least a subset of the plurality of devices, where each of the plurality of instances of the AI model has a different level of complexity, and instruct a device to execute the selected instance of the AI model.

Abstract: Systems and methods for Artificial Intelligence (AI) model characterization and offloading in a heterogenous computing platform are described. In an embodiment, an Information Handling System (IHS) may include: a heterogeneous computing platform including a plurality of devices and a memory, where the memory includes a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables it to provide a corresponding service, and where at least one of the devices operates as an orchestrator configured to: receive a characterization result from a first device, where the first device is configured to execute a first AI model configured to characterize a second AI model executed by a second device, and, prior to a subsequent execution of the second AI model, assign the second AI model to a third device based, at least in part, upon the characterization result.

Abstract: Systems and methods for usage detection and notification in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device, enables the respective device to provide a corresponding firmware service, and where at least one of the plurality of devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices and instruct one or more selected devices to execute one or more Artificial Intelligence (AI) models configured to characterize one or more workloads executed by the IHS based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods for runtime control of Artificial Intelligence (AI) model parameters in a heterogenous computing platform are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a heterogeneous computing platform comprising a plurality of devices and a memory coupled to the platform, where the memory comprises a plurality of sets of firmware instructions, where each of the sets of firmware instructions, upon execution by a respective device among the plurality of devices, enables the respective device to provide a corresponding firmware service, and where at least one of the devices operates as an orchestrator configured to: receive context or telemetry data from at least a subset of the plurality of devices, and instruct a device among the plurality of devices to modify a parameter of an AI model executed by the device based, at least in part, upon the context or telemetry data.

Abstract: Systems and methods are provided that may be implemented to automatically discover intended and/or unintended changes as they occur in an IT environment, such as a network that includes multiple endpoint information handling systems. Intended changes may include, but are not limited to, changes in user and device or endpoint system level experience metrics that occur on one or more of the endpoint information handling systems within the IT environment after a planned action occurring in the IT environment. Unintended changes to similar metrics may occur when no activities are planned or initiated for the IT environment. These IT environment changes may be discovered automatically based on individual device level telemetry data and/or fleetwide aggregations derived of benchmarking various telemetry metrics.

Abstract: Systems and methods for generating workspace distribution and/or configuration recommendations are described. In an embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive data related to a plurality of workspaces; generate, based upon the data, a recommendation to modify at least one of the plurality of workspaces; and modify the at least one of the plurality of workspaces.

Abstract: An information handling system may include at least one processor, a physical storage resource, and a non-volatile memory other than the physical storage resource. The at least one processor may be configured to execute instructions for: storing learned profile data at the physical storage resource; in response to detection of a catastrophic event, copying the learned profile data from the physical storage resource to the non-volatile memory; and subsequent to a remediation event for the catastrophic event, restoring the learned profile data from the non-volatile memory.

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: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may determine a historical discharge rate (dh) from multiple discharge rates a battery of an information handling system (IHS) while the IHS operates on electrical power provided by the battery; receive a current discharge rate (dc) of the battery; determine a full charge capacity correction factor value (Cfcc) associated with the battery; determine a weighting factor (?); determine a difference between a full charge capacity value (FCC) and Cfcc; determine ?·dh; determine (1??)·dc; determine a sum of dh·? and dc·?; determine a quotient value of FCC?Cfcc and ?·dh+(1??)·dc; scale the quotient value based at least on a relative state of charge of the battery to determine an estimated time remaining for the IHS to operate on the electrical power provided by the battery; and display the estimated time remaining.

Abstract: Systems and methods for providing workspace configuration recommendations in hoteling environments are described. In an embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive telemetry data from a plurality of client IHSs, where the telemetry data comprises integrated device data and workspace device data. The program instructions may, upon execution, further cause the IHS to identify, for a client IHS, a workspace configuration recommendation usable in a selected one of a plurality of workspaces based, at least in part, upon the telemetry data, and to transmit an indication of the recommendation to the client IHS.

Abstract: Managing firmware updates of an information handling system (IHS), including performing, at a first time, a calibration and configuration of a firmware update risk model, including: identifying predictor data associated with a plurality of IHS; training, based on the predictor data, the firmware update risk model, including generating a configuration policy including configuration rules, the configuration rules for performing computer-implemented actions responsive to a firmware update; receiving, at a second time, the firmware update at a particular IHS, and in response: identifying particular predictor data of the particular IHS; in response to identifying the particular predicator data of the particular IHS, i) accessing the firmware update risk model including the configuration policy, ii) identifying one or more of the configuration rules based on the identified particular predicator data, and iii) applying the one or more configuration rules to perform computer-implemented actions responsive to receivin

Abstract: An information handling system may include at least one processor, a physical storage resource, and a non-volatile memory other than the physical storage resource. The at least one processor may be configured to execute instructions for: storing learned profile data at the physical storage resource; in response to detection of a catastrophic event, copying the learned profile data from the physical storage resource to the non-volatile memory; and subsequent to a remediation event for the catastrophic event, restoring the learned profile data from the non-volatile memory.

Abstract: Systems and methods for workload migration recommendations in heterogeneous workspace environments are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: identify a set of workspaces launched by a set of users, among a plurality of workspaces launched by a plurality of users, where each workspace in the set of workspaces is associated with a performance or user experience metric below a threshold value; select, among a plurality of workloads executed within the set of workspaces, one or more workloads suitable for migration; and for each user of the set of users, determine whether to migrate any of the selected one or more workloads based, at least in part, upon an allocation of cloud and device resources available to the plurality of users.

Abstract: Automatically updating a full charge capacity (FCC) of a battery of an information handling system, the method including: determining that the SOC of the battery is less than the SOC threshold; determining that the time since the previous update of the FCC of the battery is greater than the time threshold; identifying configuration parameters for an update of the FCC of the battery; comparing the configuration parameters with current conditions of the battery; based on comparing the configuration parameters with current conditions of the battery, determining that the current conditions of the battery are within bounds of the configuration parameters; and in response to determining that the current conditions of the battery are within the bounds of the configuration parameters, updating the FCC of the battery.

Abstract: Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, a method may include: identifying, by an IHS, a first application; assigning a first score to the first application based upon: (i) a user's presence state, (ii) a foreground or background application state, (iii) a power adaptor state, and (iv) a hardware utilization state, detected during execution of the first application; identifying, by the IHS, a second application; assigning a second score to the second application based upon: (i) another user's presence state, (ii) another foreground or background application state, (iii) another power adaptor state, and (iv) another hardware utilization state, detected during execution of the second application; and prioritizing performance optimization of the first application over the second application in response to the first score being greater than the second score.

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: Managing firmware updates of an information handling system (IHS), including performing, at a first time, a calibration and configuration of a firmware update risk model, including: identifying predictor data associated with a plurality of IHS; training, based on the predictor data, the firmware update risk model, including generating a configuration policy including configuration rules, the configuration rules for performing computer-implemented actions responsive to a firmware update; receiving, at a second time, the firmware update at a particular IHS, and in response: identifying particular predictor data of the particular IHS; in response to identifying the particular predicator data of the particular IHS, i) accessing the firmware update risk model including the configuration policy, ii) identifying one or more of the configuration rules based on the identified particular predicator data, and iii) applying the one or more configuration rules to perform computer-implemented actions responsive to receivin

Abstract: Embodiments of systems and methods for managing an Information Handling System (IHS) based upon a proxy calendar are described. In some embodiments, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive context information, build a proxy calendar based upon the context information and in the absence of any access to calendar application data, and, in response to the proxy calendar indicating an event, perform one or more management actions associated with the event.

Abstract: Settings on an information handling system may be adjusted to set priority levels for processes executing on the information handling system in view of desired operational characteristics of the information handling system for a user persona and in view of expected future events for the information handling system. A score may be generated based on a user persona (e.g., whether a user is a light gamer, heavy gamer, corridor warrior, or desk worker) and expected future computer contexts (e.g., an expectation that a user will play a game in one hour). That score may be used to determine policies (e.g., high performance, balanced, or battery saver) to implement through settings on the computer system. Consideration of user persona classifications, associated group behaviors, and dynamic system contexts (including resource extremas, location, temporal context, and predicted future events) improve use of system resources through prioritization and governing of diverse optimization methods.

Abstract: According to one illustrative, non-limiting embodiment, a first IHS may include computer-executable instructions for performing at least one artificial intelligence (AI) service to optimize a performance of the first IHS. In response to determining that an AI workload of the AI service exceeds a specified threshold, the first IHS selects a second IHS to perform at least a portion of the AI workload, and transmits the at least one portion of the AI workload to the second IHS. When a processed AI workload is received from the second IHS, the first IHS applies one or more profile recommendations included in the processed AI workload.