What is Edge Machine Learning?

Edge ML is still a newer technology, and while it’s getting a
lot of attention, innovators are still determining how to implement
this technique across various platforms.(Vchal/iStock/Getty Images

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.


Deep learning is a subcategory of machine learning,which is a
subcategory of of artificial intelligence.

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

How Edge ML Works

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.

Unlike traditional machines, Edge ML devices will analyze and
process incoming data at the source and determine what needs to be
processed by more powerful algorithms in the Cloud, versus what can
be processed locally. For example, if you tell the Amazon Echo,
“Alexa, let’s play a game,” or “Alexa, tell me a joke,”
the games and jokes available are stored in and processed by the
device’s local hardware. This will not require sending data to
the Cloud. The device can execute the function (and keep the user
happy) without bogging down the Cloud-network. If instead, you ask
Alexa about the weather, the device will need to search an external
source (in the Cloud) for that data.

Benefits of Edge ML

Edge ML is revolutionary. It solves both security concerns
pertaining to storing personal user information in the Cloud and
also reduces strain on Cloud networks by processing data locally.
It also enables the processing of data in real-time, currently not
possible with traditional, cloud-powered smart devices, but
critical for technologies like autonomous vehicles and medical

Current and Future Applications

Edge ML is still a newer technology, and while it’s getting a
lot of attention, innovators are still determining how to implement
this technique across various platforms.

A few existing platforms include smart speakers like Amazon’s
Echo and Google’s Home. In the energy and industrial space, some
companies have developed systems with predictive sensors and
algorithms that monitor the health of the components to notify
technicians when maintenance is required. Other systems monitor for
emergencies like machine malfunctions or meltdown.

In the future, there is talk about developing Edge ML-based
systems in hospitals and assisted living facilities to monitor
things like patient heart rate, glucose levels, and falls (using
cameras and motion sensors). These technologies could be
life-saving and, if the data is processed locally at the edge,
staff would be notified in real-time when a quick response would be
essential for saving lives.

Edge ML is an exciting new technology that continues to be
talked about and developed. It will only be a matter of time before
Edge ML-powered devices (like the IoT) becomes a way of life. And
what an exciting time that will be.

Originally published by
| November 24, 2020