Abstract: Systems, methods, and computer program products that use unsupervised learning to learn relationships between operations of a machine learning model based on a model graph representation to group the operations into clusters and, given a set of clusters and labels for the clusters, use a reinforcement learning algorithm to generate a final device placement result for the machine learning model.

Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Described are a system, method, and computer program product for state compression in stateful machine learning models. The method includes receiving a transaction authorization request for a transaction and loading at least one encoded state of a recurrent neural network (RNN) model from a memory. The method further includes decoding the at least one encoded state by passing each encoded state through a decoder network to provide at least one decoded state. The method further includes generating at least one updated state and an output for the transaction by inputting at least a portion of the transaction authorization request and the at least one decoded state into the RNN model. The method further includes encoding the at least one updated state by passing each updated state through an encoder network to provide at least one encoded updated state, and storing the at least one encoded updated state in the memory.

Abstract: Provided is a system for testing a machine learning (ML) model based on simulations in an offline environment that includes at least one processor programmed or configured to receive historical transaction data, generate online simulation data, wherein generating the online simulation data includes modifying the historical timestamp of each data record to provide online simulation data. The processor is further programmed or configured to determine a timeline for a plurality of data insertion actions and a plurality of data request actions based on the online simulation data, perform a simulation of online activities involving a stateful ML model using the timeline for the plurality of data insertion actions and the plurality of data request actions, and validate the stateful ML model based on the simulation of online activities. Methods and computer program products are also provided.

Abstract: Described are a method, system, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes generating a shadow testing environment operating at least two transaction services. The method also includes receiving a plurality of transaction authorization requests. The method further includes determining a first percentage associated with a first testing policy of the first transaction service and a second percentage associated with a second testing policy of the second transaction service. The method further includes replicating in the shadow testing environment, in real-time with processing the payment transactions, a first portion of the plurality of transaction authorization requests and a second portion of the plurality of transaction authorization requests.

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: Provided are systems for ensemble learning with machine learning models that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, add an amount of time delay to one or more data instances to provide an augmented training dataset, select a first plurality of supervised machine learning models, select a second plurality of unsupervised machine learning models, train the first plurality of supervised machine learning models and the second plurality of unsupervised machine learning models based on the augmented training dataset, generate an ensemble machine learning model based on outputs of the supervised machine learning models and unsupervised machine learning models, and generate a runtime output of the ensemble machine learning model based on a runtime input to the ensemble machine learning model. Methods and computer program products are also provided.

Abstract: Described are a method, system, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes generating a shadow testing environment operating at least two transaction services. The method also includes receiving a plurality of transaction authorization requests. The method further includes determining a first percentage associated with a first testing policy of the first transaction service and a second percentage associated with a second testing policy of the second transaction service. The method further includes replicating in the shadow testing environment, in real-time with processing the payment transactions, a first portion of the plurality of transaction authorization requests and a second portion of the plurality of transaction authorization requests.

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.