Edge-Based Discovery of Training Data for Machine Learning

We show how edge-based early discard of data can greatly improve the productivity of a human expert in assembling a large training set for machine learning. This task may span multiple data sources that are live (e.g., video cameras) or archival (data sets dispersed over the Internet). The critical resource here is the attention of the expert. We describe Eureka, an interactive system that leverages edge computing to greatly improve the productivity of experts in this task. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.