Abstract: A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.

Abstract: Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment.

Abstract: A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.

Abstract: Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment.

Abstract: Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment.

Abstract: A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.

Abstract: Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment.

Abstract: A method, system, and computer program product for maintaining model state at model data centers hosting a same machine learning model may receive first input data input, at a first time, to a first implementation of a model to generate first output data, the first implementation of the model being associated with a first model state at a time before the first time; receive second input data input, at a second time different than the first time, to a second implementation of the model to generate second output data, the second implementation of the model being associated with a second model state at a time before the second time; determine, based on the first input data and the second input data, update data for the first model state of the first implementation and the second model state of the second implementation; and provide, at a third time subsequent to the first time and the second time, the update data.

Abstract: A virtual system environment in a computing system allows non-root users to perform administrative operations normally requiring root privileges. A virtual control module has a virtual file system with virtual directories corresponding to one or more of the system directories of a root file system. Each virtual directory contains one or more symbolic links symbolically linking to one or more system files as link targets. The symbolic links have non-root permissions that allow the non-root users to perform administrative operations in the virtual control module that delete, change or replace the symbolic links. A capability is provided to resolve the symbolic links within the virtual control module to allow the non-root users to execute the link targets via the symbolic links. One or more non-root users may be placed in the virtual control module by setting their apparent root directory to a root directory of the virtual file system.

Abstract: A virtual system environment in a computing system allows non-root users to perform administrative operations normally requiring root privileges. A virtual control module has a virtual file system with virtual directories corresponding to one or more of the system directories of a root file system. Each virtual directory contains one or more symbolic links symbolically linking to one or more system files as link targets. The symbolic links have non-root permissions that allow the non-root users to perform administrative operations in the virtual control module that delete, change or replace the symbolic links. A capability is provided to resolve the symbolic links within the virtual control module to allow the non-root users to execute the link targets via the symbolic links. One or more non-root users may be placed in the virtual control module by setting their apparent root directory to a root directory of the virtual file system.

Abstract: An information handling system (IHS) employs operating system software to manage IHS resources. The operating system software manages software application programs as processes executing within the IHS. The processes run in foreground and background mode within the IHS. Processes running in foreground mode are subject to hang-up events with negative process output results, such as output data loss. In one embodiment, the operating system software supports a “no hang-up now” command for use with processes running in foreground mode. The “no hang-up now” command provides system users the ability to hang-up or log-out of an IHS terminal without negative effects on the current foreground process. A user may invoke the “no hang-up now” command after execution of the foreground process is already underway. The no hang-up command moves the foreground application to the background for continued execution. A signal handler program prevents termination of the background process until the process completes.

Abstract: An information handling system (IHS) employs operating system software to manage IHS resources. The operating system software manages software application programs as processes executing within the IHS. The processes run in foreground and background mode within the IHS. Processes running in foreground mode are subject to hang-up events with negative process output results, such as output data loss. In one embodiment, the operating system software supports a “no hang-up now” command for use with processes running in foreground mode. The “no hang-up now” command provides system users the ability to hang-up or log-out of an IHS terminal without negative effects on the current foreground process. A user may invoke the “no hang-up now” command after execution of the foreground process is already underway. The no hang-up command moves the foreground application to the background for continued execution. A signal handler program prevents termination of the background process until the process completes.