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.
Former pentester and bug bounty hunter Nenan Zaric joins the show to talk about the types of vulnerabilities that companies should be looking for and about how to automate security workflows through the Trickest platform, a company he founded.
Bjorn Ovick joins the show to share his background, thoughts on the evolution of technology in this space, break down PCI DSS, payment processors, and how Skyflow helps not only offload PCI compliance but gives businesses flexibility to work with multiple payment processors.
Google Cloud Principle Architect Anjali Khatri and Google Cloud Solutions Engineer Nitin Vashishtha join the show to discuss DevOps, DevSecOps, the shift left movement, and how to use Google Cloud to create a secure CI/CD pipeline.