Abstract: Systems and methods are provided for identifying a safe window for creating a firmware update package. 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: generate tokens based upon a log of historical firmware update package creation events; identify a plurality of workflows based upon the tokens; predict, based upon the plurality of workflows, a future workload of a firmware update package creation service; and indicate, based upon the future workload, a time window for creating a firmware update package using the firmware update package creation service that increases a probability of successful creation.

Abstract: A storage usage management system includes a storage usage management subsystem coupled to first and second storage devices. The storage usage management subsystem collects first write usage data from use of the first storage device by a host mapped to the first storage device, determines that second write usage data from use of the first storage device by the host exceeds first metric(s) associated with the first write usage data by a first threshold, and collects the second write usage data and third write usage data from immediately subsequent use of the first storage device by the host. If the storage usage management subsystem determines that the second and third write usage data exceed second metric(s) associated with the first write usage data by a second threshold, it uses the second and third write usage data to determine whether to remap the host to the second storage device.

Abstract: An information handling system may have a long short term memory (LSTM) that receives Input/Output (I/O) parameters, and produces a prediction output by operation of a recursive neural network (RNN). An I/O optimizer provides the I/O parameters to the LSTM and receives the prediction output from the LSTM. The I/O optimizer may include a manager module configured to provide control signals to control gates for controlling application of the I/O parameters and the prediction output, and a collector module configured to collect the I/O parameters.

Abstract: A storage usage management system includes a storage usage management subsystem coupled to first and second storage devices. The storage usage management subsystem collects first write usage data from use of the first storage device by a host mapped to the first storage device, determines that second write usage data from use of the first storage device by the host exceeds first metric(s) associated with the first write usage data by a first threshold, and collects the second write usage data and third write usage data from immediately subsequent use of the first storage device by the host. If the storage usage management subsystem determines that the second and third write usage data exceed second metric(s) associated with the first write usage data by a second threshold, it uses the second and third write usage data to determine whether to remap the host to the second storage device.

Abstract: A system comprising a dynamically configurable neural network engine operating on a plurality of processors, an automated knowledge matrix creation system operating on a processor and coupled to the dynamically configurable neural network engine, the automated knowledge matrix creation system configured to provide an input to the neural network engine, and a machine comprehension system coupled to the dynamically configurable neural network engine and configured to associate a plurality of product development documents with a customer created knowledge base and to provide input to the neural network engine.

Abstract: Systems and methods for tiering data in distributed data networks. A global model is developed based on federated learning where edge servers are able to train a model. The learning from the edge servers are collectively applied to the global model. This process can be repeated until the global model is ready for deployment. The global model allows data to be tiered. This may include pushing data from a datacenter to edge servers or cleaning edge servers of data that does not satisfy the global model. The model can be retrained and can be used to proactively push new content out to the edge servers.

Abstract: Systems and methods for wholesale client onboarding are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for client onboarding may include: (1) creating know your customer requirements and trigger conditions for a client based on user configurable requirements and a rules engine; (2) receiving know your customer data for the client from a client data exchange module; (3) triggering the receipt of additional know your customer data in response to a trigger condition being met by the received know your customer data; (4) receiving document data from a document digitization module; (5) validating the know your customer data and the document data; and (6) auto populating a know your customer record with the validated know your customer data and the document data.

Abstract: Systems and methods are provided for identifying a safe window for creating a firmware update package. 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: generate tokens based upon a log of historical firmware update package creation events; identify a plurality of workflows based upon the tokens; predict, based upon the plurality of workflows, a future workload of a firmware update package creation service; and indicate, based upon the future workload, a time window for creating a firmware update package using the firmware update package creation service that increases a probability of successful creation.

Abstract: Systems and methods are provided for identifying a safe window for creating a firmware update package. 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: generate tokens based upon a log of historical firmware update package creation events; identify a plurality of workflows based upon the tokens; predict, based upon the plurality of workflows, a future workload of a firmware update package creation service; and indicate, based upon the future workload, a time window for creating a firmware update package using the firmware update package creation service that increases a probability of successful creation.

Abstract: Systems and methods for tiering data in distributed data networks. A global model is developed based on federated learning where edge servers are able to train a model. The learning from the edge servers are collectively applied to the global model. This process can be repeated until the global model is ready for deployment. The global model allows data to be tiered. This may include pushing data from a datacenter to edge servers or cleaning edge servers of data that does not satisfy the global model. The model can be retrained and can be used to proactively push new content out to the edge servers.

Abstract: An information handling system may have a long short term memory (LSTM) that receives Input/Output (I/O) parameters, and produces a prediction output by operation of a recursive neural network (RNN). An I/O optimizer provides the I/O parameters to the LSTM and receives the prediction output from the LSTM. The I/O optimizer may include a manager module configured to provide control signals to control gates for controlling application of the I/O parameters and the prediction output, and a collector module configured to collect the I/O parameters.

Abstract: Systems and methods for wholesale client onboarding are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for client onboarding may include: (1) creating know your customer requirements and trigger conditions for a client based on user configurable requirements and a rules engine; (2) receiving know your customer data for the client from a client data exchange module; (3) triggering the receipt of additional know your customer data in response to a trigger condition being met by the received know your customer data; (4) receiving document data from a document digitization module; (5) validating the know your customer data and the document data; and (6) auto populating a know your customer record with the validated know your customer data and the document data.

Abstract: A system comprising a dynamically configurable neural network engine operating on a plurality of processors, an automated knowledge matrix creation system operating on a processor and coupled to the dynamically configurable neural network engine, the automated knowledge matrix creation system configured to provide an input to the neural network engine, and a machine comprehension system coupled to the dynamically configurable neural network engine and configured to associate a plurality of product development documents with a customer created knowledge base and to provide input to the neural network engine.

Abstract: Systems and methods are provided that that may be implemented to perform generalized adaptive storage endpoint prediction. A system/storage management application gathers data samples pertaining to operation of a storage device (e.g., remaining rated write endurance, available spare blocks, remaining drive space), clusters the data samples (e.g., using DBSCAN algorithm), and approximates a polynomial function usable to predict an endpoint of the storage device (e.g., SSD) by using an artificial neural network that receives the data samples and clusters and in response generates the approximated polynomial function. The approximate polynomial function may be a combination of Gaussian functions corresponding to the clusters. A mean and variance associated with each cluster may be calculated that is a mean and variance of a corresponding Gaussian function. A feed forward artificial neural network having at least one hidden layer, constant bias, and Gaussian activation function may be employed.