Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to optimize resources in edge networks. An example apparatus includes agent managing circuitry to invoke an exploration agent to identify platform resource devices, select a first one of the identified platform resource devices, and generate first optimization metrics for the workload corresponding to the first one of the identified platform resource devices, the first optimization metrics corresponding to a first path. The example agent is to further select a second one of the identified platform resource devices, generate second optimization metrics for the workload corresponding to the second one of the identified platform resource devices, the second optimization metrics corresponding to a second path.

Abstract: Methods, apparatus, systems, and articles of manufacture for data enhanced automated model generation are disclosed. Example instructions, when executed, cause at least one processor to access a request to generate a machine learning model to perform a selected task, generate task knowledge based on a previously generated machine learning model, create a search space based on the task knowledge, and generate a machine learning model using neural architecture search, the neural architecture search beginning based on the search space.

Abstract: Disclosed are embodiments providing model data specific to particular object recognition client environments. Model data capable of performing well under all possible conditions can be large, slow, and less accurate, at least in some circumstances. The disclosed embodiments solve this problem by providing smaller, more precisely designed models that are adapted to specific conditions faced by an object recognition client. These smaller, more precise models are extracted, in response to a request for said model data, from a super-net trained via data matching one or more environmental conditions specified by the object recognition client. By providing the client with model data tailored to its specific needs, several technical benefits are achieved, such as higher object recognition accuracy with less processing overhead.

Abstract: A system and related method are provided for sensor-based person recognition. The system has a data store including global data of a user, the global data including a globally unique identifier and sensed training data. The system further has a classifier to identify the user at a first time using the global data, augment the global data with first context information of the user captured at the first time, and identify the user at a second time by finding correlations between second context information and the first context information when using the global data fails, the second context information of the user captured at the second time.

Abstract: A system and related method are provided for sensor-based person recognition. The system has a data store including global data of a user, the global data including a globally unique identifier and sensed training data. The system further has a classifier to identify the user at a first time using the global data, augment the global data with first context information of the user captured at the first time, and identify the user at a second time by finding correlations between second context information and the first context information when using the global data fails, the second context information of the user captured at the second time.