Edge devices are hardware devices that sit at the edge of a network. They could be routers, switches, your phone, voice assistant, or even a sensor in a factory that monitors factory conditions.

Machine learning on the edge combines ideas from machine learning with embedded engineering. With machine learning models running on edge devices amazing new types of applications can be built, such as using image recognition to only take pictures of the objects you care about, developing self-driving cars, or automatically detect potential equipment failure.

However, with more and more edge devices being used all the time that might be collecting sensitive information via sensors, there are a number of potential privacy and security concerns.

Dan Situnayake, Head of Machine Learning at Edge Impulse, joins the show to share his knowledge about the practical privacy and security concerns when working with edge IoT devices and how to still leverage this incredible technology but do so in an ethical and privacy-preserving way.

Topics:

• What’s your background and how did you end up as the head of machine learning at Edge Impulse?

Resources:

• Edge Impulse