Abstract: Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on the likelihood of individuals posing a security risk. To identify the security risks of the individuals, the paths of the individuals may be tracked and compared to typical paths through environments that are followed by most individuals that traverse through the environment. The individuals following a typical path may not indicate a security risk and a resource prioritization may be retained or decreased for the individuals not posing a security risk. The individuals not following a typical path may indicate a security risk and a resource prioritization may be increased for the individuals posing a security risk.

Abstract: An apparatus comprises at least one processing device configured to determine, for a first set of artificial intelligence (AI) workloads, a first set of metrics characterizing AI models utilized by the first set of AI workloads and a second set of metrics characterizing power consumption associated with execution of the first set of AI workloads. The at least one processing device is also configured to generate, based on the first and second sets of metrics, a predictive model configured for generating power consumption predictions for a second set of AI workloads, to utilize the predictive model to generate, for a given one of the second set of AI workloads, predicted power consumption associated with execution of the given AI workload on one or more information technology (IT) assets, and to control execution of the given AI workload on a given IT asset based on the predicted power consumption.

Abstract: An apparatus comprises at least one processing device configured to generate a first data structure comprising thermal imaging data for an area of an information technology infrastructure environment obtained from thermal imaging sensors, and to process, utilizing at least one thermal anomaly detection machine learning model, at least a portion of the first data structure to generate a second data structure characterizing thermal anomalies detected in the area of the information technology infrastructure environment. The at least one processing device is further configured to select remedial actions to be performed in the information technology infrastructure environment for addressing the thermal anomalies detected in the area of the information technology infrastructure environment, and to perform at least one of the selected remedial actions in the information technology infrastructure environment.

Abstract: An apparatus comprises at least one processing device configured to generate a first data structure comprising thermal imaging data for an area of an information technology infrastructure environment obtained from thermal imaging sensors, and to process, utilizing at least one thermal anomaly detection machine learning model, at least a portion of the first data structure to generate a second data structure characterizing thermal anomalies detected in the area of the information technology infrastructure environment. The at least one processing device is further configured to select remedial actions to be performed in the information technology infrastructure environment for addressing the thermal anomalies detected in the area of the information technology infrastructure environment, and to perform at least one of the selected remedial actions in the information technology infrastructure environment.

Abstract: A method for managing hardware resources comprises obtaining, by a computer vision (CV) manager, a request for a federated CV regression model, in response to the request: performing an initial training of the federated CV regression model using an initial training dataset to obtain an initial federated CV regression model, sending training requests to two local hardware resource systems, wherein each local hardware resource system implements a local camera system and a processing system, and wherein the training request comprises training a local CV regression model based on the processing system and the local camera system, obtaining the first local CV regression model and the second local CV regression model, generating the federated CV regression model using the two local CV regression models, and distributing the federated CV regression model to the first local hardware resource system and the second local hardware resource system.

Abstract: Techniques described herein relate to a method for allocating graphics processing unit partitions for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial graphics processing unit (GPU) partition allocation request associated with a CV workload; in response to obtaining the initial GPU partition allocation request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the GPU partition allocation request; generating an optimal GPU partition allocation based on the first CV environment configuration information and the CV workload information using a GPU partition model; and initiating performance of the CV workload in a CV environment based on the optimal GPU partition allocation.

Abstract: An apparatus comprises at least one processing device configured to determine, for a first set of artificial intelligence (AI) workloads, a first set of metrics characterizing AI models utilized by the first set of AI workloads and a second set of metrics characterizing power consumption associated with execution of the first set of AI workloads. The at least one processing device is also configured to generate, based on the first and second sets of metrics, a predictive model configured for generating power consumption predictions for a second set of AI workloads, to utilize the predictive model to generate, for a given one of the second set of AI workloads, predicted power consumption associated with execution of the given AI workload on one or more information technology (IT) assets, and to control execution of the given AI workload on a given IT asset based on the predicted power consumption.

Abstract: Methods and systems for managing threats to data processing systems are disclosed. To manage the threats, multiple threat management models may be utilized. The threat management models may include centralized models that rely on operable connectivity to particular systems, and distributed models that do not rely on operable connectivity to the particular systems. The data processing systems may flexibly switch between use of these models to respond to changes in operably connectivity of a distributed system.

Abstract: A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying, by a computer vision (CV) node of a plurality of CV nodes, a CV alert; in response to identifying the CV alert: identifying candidate CV nodes of the plurality of CV nodes; generating a dynamic CV zone using the candidate CV nodes; initiating performance of a CV workload by the dynamic CV zone to generate CV data associated with the CV alert; generating a CV alert case associated with the CV alert; obtaining CV data from the dynamic CV zone; updating the CV alert case using the CV data generated during the performance of the CV workload; and providing the updated CV alert case to a VMS.

Abstract: Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on a trained state of a data processing system to modify a level of surveillance of the persons. By doing so, the device management services may only be provided when certain conditions are met rather than continuously. To place the data processing system in the trained state, the data processing system may need to be trained to predict paths that individuals are likely to traverse using training data. To obtain the training data, previously traversed paths of individuals may be tracked through an environment and stored into a database.

Abstract: Techniques described herein relate to a method for optimizing power for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial power optimization request associated with a CV workload; in response to obtaining the initial power optimization request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the power optimization request; generating a power optimization report based on the first CV environment configuration information and the CV workload information using a power optimization model; and initiating performance of the CV workload in a CV environment based on the power optimization report.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; in response to identifying the CV alert: making a first determination that the CV node is not participating in a distributed workload associated with a higher priority CV alert; in response to the first determination, the CV node: selects candidate CV nodes of the plurality of CV nodes; initiates performance of the distributed CV workload by the candidate CV nodes to generate CV data associated with the CV alert; generates a CV alert case associated with the CV alert; obtains CV data from the candidate CV nodes that are performing the distributed CV workload; updates the CV alert case using the CV data generated during the performance of the distributed CV workload; and provides the updated CV alert case to a VMS.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; generating a CV alert case associated with the CV alert; identifying nearby CV nodes of the plurality of CV nodes; transmitting CV alert to the nearby CV nodes; for each of the nearby CV nodes: receiving the CV alert; determining, based on CV environment configuration information of the nearby CV node and the CV alert, whether to perform a distributed CV workload; when the determination is to perform the distributed CV workload: initiating performance of the distributed CV workload by the nearby CV nodes to generate CV data; updating the CV alert case using CV data generated during the performance of the distributed CV workload to obtain an updated CV alert case; and transmitting by the nearby CV node to the VMS the updated CV alert case.

Abstract: Methods and systems for providing computer implemented services using inference models are disclosed. The inference models may be obtained through federated learning, and may be used to generate output used in the computer implemented services. During the federated learning, instances of inference models may be generated using siloed data with distribution restrictions. Some of the instances of the inference models may be selected for continued learning to obtain a final inference model used to generate the output.

Abstract: A method for managing hardware resources comprises obtaining, by a computer vision (CV) manager, a request for a federated CV regression model, in response to the request: performing an initial training of the federated CV regression model using an initial training dataset to obtain an initial federated CV regression model, sending training requests to two local hardware resource systems, wherein each local hardware resource system implements a local camera system and a processing system, and wherein the training request comprises training a local CV regression model based on the processing system and the local camera system, obtaining the first local CV regression model and the second local CV regression model, generating the federated CV regression model using the two local CV regression models, and distributing the federated CV regression model to the first local hardware resource system and the second local hardware resource system.

Abstract: Methods and systems for managing threats to data processing systems are disclosed. To manage the threats, multiple threat management models may be utilized. The threat management models may include centralized models that rely on operable connectivity to particular systems, and distributed models that do not rely on operable connectivity to the particular systems. The data processing systems may flexibly switch between use of these models to respond to changes in operably connectivity of a distributed system.

Abstract: A system comprising a processor, a processing system operatively connected to the processor, a processing system storage, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a metadata analysis of the encoded data to obtain metadata associated with the encoded data, and store the metadata in the processing system storage. The processing system is programmed to obtain the metadata from the processing system storage, perform a computer vision (CV) inferencing on the encoded data using the metadata to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system includes a processor, a processing system operatively connected to the processor, comprising a decoding-dedicated hardware component, an operating system operating on the processor and not on the processing system, wherein the processing system is programmed to obtain encoded data from a local data source, perform, using the decoding-dedicated hardware component, a decoding of the encoded data to obtain decoded data, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.