Edge filtering of machine learning training data

Abstract

A data processing solution for data generated by autonomous vehicles, unmanned aerial vehicles (UAVs), and other Internet of Things (IoT) devices may include a network of edge devices and one or more cloud servers to gather and process data. Initially, all data generated by the edge devices may be gathered, transported, and processed by the cloud server(s) to train machine learning models. The cloud server(s) may push machine learning (ML) models to the network of edge devices. Using the ML models running on the edge devices, the data gathered by the individual edge devices may be filtered by its relevance score, which correlates to the impact the data would have as training data. The relevance score is used by the edge device to identify the data to upload to the cloud.