What is Edge Machine Learning?
Edge Machine Learning (Edge ML) is one of the most talked-about tech advancements since the Internet of Things (IoT), and for a good reason. With the rise of IoT came an explosion of Smart Devices connected to the Cloud, but the network was not yet ready to support this surge in demand. Cloud networks were congested, and companies overlooked key issues with Cloud computing, such as security. The solution: Edge ML.
So, what is Edge ML anyway? Edge ML is a technique by which Smart Devices can process data locally (either using local servers or at the device-level) using machine and deep learning algorithms, reducing reliance on Cloud networks. The term edge refers to processing that occurs at the device- or local-level (and closest to the components collecting the data) by deep- and machine-learning algorithms.
Edge devices do still send data to the Cloud when needed, but the ability to process some data locally allows for screening of the data sent to the Cloud while also making real-time data processing (and response) possible.
Artificial intelligence, defined broadly, is the field of training machines to autonomously perform tasks normally thought to require intelligence. Beneath that umbrella is machine learning, in which machines autonomously learn new tasks. Deep learning is a subcategory of machine learning. It involves training machines to process information in a way that mimics the way the human brain learns new things.
Edge ML relies on both machine learning and deep learning algorithms to locally process data, depending on the application.
Before Edge ML came about, smart devices would send all data to the Cloud (see IEEE arXiv:200317172v2). You’ve probably heard the term Big Data. Named after the massive influx of datasets that resulted in part from the IoT, Big Data has become a growing field that attempts to structure and make sense of massive datasets. The processing of this data, such as critical datasets in the medical and industrial sectors, will vastly improve things like the ability to predict and respond (almost) immediately to emergencies. Much of the data collected, however, is superfluous.
