Abstract: A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.

Abstract: A process can include transmitting a pre-trained machine learning model to an edge compute unit associated with a request. The edge compute unit can perform inference using the pre-trained machine learning model and one or more sensor data streams obtained at an edge location. One or more batch uploads of information can be received, associated with inference performed by the edge compute unit and using the pre-trained machine learning model. One or more updated machine learning models can be generated, based on using the batch uploads of information from the edge compute unit to retrain or finetune the pre-trained machine learning model. The one or more updated machine learning models can be transmitted to the edge compute unit, wherein transmission of the updated machine learning model is responsive to receiving the one or more batch uploads of information.

Abstract: A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.

Abstract: A process can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: A method can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration associated with at least one edge compute unit of the fleet. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: A process can include obtaining a plurality of data points each associated with a respective edge device of a fleet of edge devices, each respective edge device associated with an edge site location or edge device asset group. The plurality of data points are stored to a fleet map data catalog and a filtering selection for viewing a filtered subset of the fleet map data catalog is received, indicating a selected geographic view area and selected edge device types from a plurality of edge device types. Data points corresponding to the filtered subset are obtained from the fleet map data catalog using the filtering selection. A fleet map GUI view is generated using the data points corresponding to the filtered subset, the fleet map GUI view comprising a converged geographic map of the selected geographic view area, with data points are rendered at corresponding locations within the converged geographic map.

Abstract: A process can include transmitting a pre-trained machine learning model to an edge compute unit associated with a request. The edge compute unit can perform inference using the pre-trained machine learning model and one or more sensor data streams obtained at an edge location. One or more batch uploads of information can be received, associated with inference performed by the edge compute unit and using the pre-trained machine learning model. One or more updated machine learning models can be generated, based on using the batch uploads of information from the edge compute unit to retrain or finetune the pre-trained machine learning model. The one or more updated machine learning models can be transmitted to the edge compute unit, wherein transmission of the updated machine learning model is responsive to receiving the one or more batch uploads of information.

Abstract: An apparatus can be configured to receive monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.

Abstract: A process can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: An apparatus can be configured to receive monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration associated with at least one edge compute unit of the fleet. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: A method can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration associated with at least one edge compute unit of the fleet. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: A process can include obtaining a plurality of data points each associated with a respective edge device of a fleet of edge devices, each respective edge device associated with an edge site location or edge device asset group. The plurality of data points are stored to a fleet map data catalog and a filtering selection for viewing a filtered subset of the fleet map data catalog is received, indicating a selected geographic view area and selected edge device types from a plurality of edge device types. Data points corresponding to the filtered subset are obtained from the fleet map data catalog using the filtering selection. A fleet map GUI view is generated using the data points corresponding to the filtered subset, the fleet map GUI view comprising a converged geographic map of the selected geographic view area, with data points are rendered at corresponding locations within the converged geographic map.

Abstract: An apparatus can be configured to receive monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: A process can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

Abstract: Various embodiments enable automated testing of Application Program Interfaces (APIs) by abstracting API call signatures and processing the abstracted API call signatures utilizing one or more genetic algorithms. Utilizing the inventive approach, test cases are built and then analyzed using a genetic algorithm. This can be done to both identify problems, such as bugs, associated with the APIs, and/or to identify quality improvements.

Abstract: In one embodiment, a test case run analyzer may filter out failure events with known causes from a test report. The test case run analyzer may receive a test report of a test case run of an application process. The test case run analyzer may automatically identify a failure event in the test case run. The test case run analyzer may automatically compare the failure event to a failure pattern set. The test case run analyzer may filter the test report based on the failure pattern set.

Abstract: Various embodiments enable automated testing of Application Program Interfaces (APIs) by abstracting API call signatures and processing the abstracted API call signatures utilizing one or more genetic algorithms. Utilizing the inventive approach, test cases are built and then analyzed using a genetic algorithm. This can be done to both identify problems, such as bugs, associated with the APIs, and/or to identify quality improvements.