Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

Abstract: A method and system 1 are provided for collecting and monitoring shop floor information. In this method and system, a shop floor information collector 108 and a working machine 107 are provided to each production line for collecting real time information, such as real time output 2022, defective product category and quantity 2024, working status 2028, and the like. A central management unit 20 is provided to store and process received data. The central management unit can obtain outside data by a communication network 2 and may be accessed by a plurality of clients 5. A monitoring module 30 is provided to detect defective data on production lines and send an alarm signal to a repair station.

Abstract: A method and system 1 are provided for collecting and monitoring shop floor information. In this method and system, a shop floor information collector 108 and a working machine 107 are provided to each production line for collecting real time information, such as real time output 2022, defective product category and quantity 2024, working status 2028, and the like. A central management unit 20 is provided to store and process received data. The central management unit can obtain outside data by a communication network 2 and may be accessed by a plurality of clients 5. A monitoring module 30 is provided to detect defective data on production lines and send an alarm signal to a repair station.