Abstract: Techniques described herein relate to a method for optimizing workflow execution. The method may include receiving an event notification at a service controller, wherein the event notification is associated with a workflow deployed in a device ecosystem; performing, based on receiving the event notification, a workflow reconfiguration action comprising: providing a first workflow portion to a first platform controller in a first domain in the device ecosystem; transmitting a workflow reconfiguration action notification to a second platform controller in a second domain of the device ecosystem; identifying, by the second platform controller and in response to receiving the workflow reconfiguration action notification, a data transfer optimization action associated with data to be transmitted from the second domain to the first domain and used during execution of the first workflow portion; and transmitting the data from the second domain to the first domain using the data transfer optimization action.

Abstract: Self-provisioning a computer system is illustrated. Code in a secure base activation image implements an auto-connection service at the computer system. The auto-connection service attempts to perform a local activation by attempting to determine that a private port is open and using the private port, initiating an identification and attestation process with an activation service. The auto-connection service performs a remote activation by determining that the private port is closed, defaulting activation to a public port, and using the public port, initiating an identification and attestation process with the activation service.

Abstract: A method of provisioning a self-provisioning computer system is disclosed. The method includes executing code in a secure base activation image to perform various functions. This includes executing an identification process, using a cryptographically created identifier included in the base activation image, with an activation service to confirm an identity of the computer system with the activation service. This further includes confirming system integrity of the computer system with the activation service. Based on confirming the identity of the computer system and confirming system integrity of the computer system, The computer system is unlocked for load installation. Load installation is performed by providing capabilities for the computer system to the activation service and receiving the load based on the provided capabilities.

Abstract: A method of implementing a self-provisioning computer system is shown. The method includes storing a secure base activation image on a computer system. This includes storing executable code to implement enhanced network discovery configured to first attempt to determine if a private TCP port is open and if the private TCP port is not open to default to use of a public URL on a well-known TCP port at the computer system. This further includes storing executable code to implement an auto-connection service configured to automatically connect to a remote activation service to perform identification and attestation of the computer system. This further includes storing executable code to implement a secure vault for cryptographic secrets.

Abstract: Embodiments of systems and methods for automated identification of training datasets 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: receive a training dataset comprising a plurality of elements, tag an element of the training dataset with: (a) a first attribute representing a first characteristic detectable in the element, and (b) a second attribute representing a second characteristic not detectable in the element, and select a subset of the plurality of elements to train an Artificial Intelligence (AI) or Machine Learning (ML) model based, at least in part, upon the tag.

Abstract: Embodiments of systems and methods for model prediction confidence utilizing drift 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 drift with respect to an Artificial Intelligence (AI) or Machine Learning (ML) model; and adjust a confidence score of a prediction or inference produced by the AI/ML model based, at least in part, upon the drift.

Abstract: Embodiments of systems and methods for unsupervised data characterization utilizing drift 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: provide input data to an Artificial Intelligence (AI) or Machine Learning (ML) drift detector, where the AI/ML drift detector is associated with a characteristic undetectable in the input data; and receive a drift confidence score from the AI/ML drift detector.

Abstract: Embodiments of systems and methods for enhanced drift remediation with causal methods and online model modification 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: detect drift in an Artificial Intelligence (AI) or Machine Learning (ML) model configured to make a prediction or a causal reasoning graphical or structural inference based upon input data, identify a root cause of the drift, and tag the input data with an indication of the root cause.

Abstract: Embodiments of systems and methods for multi-observer, consensus-based ground truth 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: detect an observation overlap between two or more devices; identify a consensus between Artificial Intelligence (AI) or Machine Intelligence (ML) model inferences made based upon data received by the two or more devices; and in response to the identification, tag at least a subset of the data with a ground truth label.

Abstract: Techniques described herein relate to a method for distributed data management. The method may include obtaining, by a data management agent of a data host and from an application executing on the data host, a request to access data; obtaining, by the data management agent, an information set associated with the data; making a determination, by the data management agent, that at least a portion of the data is not ready to be used by the application; and performing, by the data management agent and based on the determination, a data preparation action set.

Abstract: Techniques described herein relate to a method for deploying workflows. The method may include receiving, by a global orchestrator of a device ecosystem, a request to execute a workflow; decomposing, by the global orchestrator, the workflow into a plurality of workflow portions; executing, by the global orchestrator, a metaheuristic algorithm to generate a result comprising a plurality of domains of the device ecosystem in which to execute the plurality of workflow portions; and providing, by the global orchestrator, the plurality of workflow portions to respective local orchestrators of the plurality of domains based on the result of executing the metaheuristic algorithm.

Abstract: Techniques described herein relate to a method for deploying workflows. The method may include receiving, at a service controller of a federated controller, a request to deploy a workflow in a device ecosystem; decomposing, by the service controller, the workflow into a plurality of workflow portions; performing, by the service controller, a search in a capabilities and constraints data construct to identify a domain in which to perform a workflow portion of the plurality of workflow portions; providing the workflow portion and workflow constraints to a platform controller in the domain; performing, by the platform controller, a subgraph similarity check to determine that a previously executed workflow portion was successfully executed on a subgraph in the domain; provisioning, by the platform controller, a set of devices in the domain based on the subgraph; and executing the workflow portion in the domain.

Abstract: Techniques described herein relate to a method for distributed data management. The method may include obtaining data descriptors for an application executing on a data host, performing a dataset policy analysis using the data descriptors to determine a data placement for a dataset associated with the application using a global name repository, performing, based on the data policy analysis, the data placement, and based on the data placement, updating the global name repository.

Abstract: Techniques described herein relate to a method for distributed data management. The method may include making a first determination that data is written to a data structure of storage of a data host; obtaining, based on the first determination, data information associated with the data; making a second determination of intent corresponding to the data; generating a global name and metadata corresponding to the data, wherein the metadata comprises the intent; and publishing the global name and the metadata to a global name repository.

Abstract: Techniques described herein relate to a method for using pipeline data for deploying workflows. The method may include determining that a pipeline testing trigger occurred for a workflow; decomposing a pipeline testing manifest of the workflow; generating a testing execution plan using the decomposed workflow; adding instrumentation to the testing execution plan; determining that the instrumented testing execution plan is valid; deploying computing devices within a CI/CD pipeline ecosystem for performing the instrumented testing execution plan; capturing deployment logs; initiating telemetry capture; executing pipeline testing of the workflow based on the instrumented testing execution plan; generating a pipeline deployment information set based on the pipeline testing; and providing the pipeline deployment information set to an orchestrator of a production device ecosystem.

Abstract: Techniques described herein relate to a method for deploying workflows. The method may include receiving, by a global orchestrator of a device ecosystem, a request to execute a workflow; decomposing, by the global orchestrator, the workflow into a plurality of workflow portions; executing, by the global orchestrator, a metaheuristic algorithm to generate a result comprising a plurality of domains of the device ecosystem in which to execute the plurality of workflow portions; and providing, by the global orchestrator, the plurality of workflow portions to respective local orchestrators of the plurality of domains based on the result of executing the metaheuristic algorithm.

Abstract: A system for processing data is disclosed that includes an artificial intelligence (AI) model operating on a processor and configured to process an incoming data set to generate a data output. An AI model anomaly detection system operating on the processor and configured to receive the incoming data set and the data output and to generate an anomaly detection as a function of the incoming data set and the data output. An AI model anomaly analysis system operating on the processor and configured to receive the anomaly detection and the incoming data set and to generate AI model anomaly data. An AI model anomaly mitigation system operating on the processor and configured to receive the AI model anomaly data and to generate AI model anomaly correction data.

Abstract: Techniques described herein relate to a method for performing data services. The method may include receiving, from a monitor agent, a global name and metadata corresponding to data written to a data structure of storage of a data host; performing a services assessment using the global name, the metadata, and a data service policy rule to obtain at least one data service to perform on the data; and performing the at least one data service based on the services assessment.

Abstract: Techniques described herein relate to a method for deploying workflows.

Abstract: Techniques described herein relate to a method for deploying workflows. The method may include receiving, by a platform controller of a domain, a workflow portion and workflow constraints from a service controller of a federated controller, provisioning, by the platform controller, a set of devices in the domain to the workflow portion, executing the workflow portion in the domain using the set of devices, monitoring the executing of the workflow portion to determine an occupancy of the workflow portion, making a determination that the occupancy of the workflow portion exceeds an available capacity of the domain, providing, based on the determination, the occupancy to the service controller, and in response to the occupancy, performing, by the service controller, a remediation action.