Zaggy AI is now a part of Microsoft for Startups Founders Hub

Nothing too crazy to report in this patch-level release: Latest package has been published on PyPi and latest docs are up. Let us know if you have any issues or feature requests on our GitHub project page!

Zaggy AI is proud to announce our first FOSS contribution, PashehNet. PashehNet is a tool for quickly and reproducibly creating simulated sensor networks (SSN) that can publish to a target system.

Leveraging AI at the edge is challenging at best. We see many IoT solutions trying to push AI all the way to the actual sensors, or all the way into the cloud. We think there is a better solution.

In an era where automation, precision, and real-time monitoring have become paramount, industrial sensors stand as unsung heroes. These small, yet potent devices, have profoundly revolutionized industries, driving operational efficiencies, reducing manual labor, and enhancing product quality. So, what exactly are industrial sensors, and how do they fit into the grand scheme of modern manufacturing?

By developing characterization approaches for drivers, vehicles, and tracks, we can better ascertain if each major component is playing its part to perfection.

Deep learning, a subset of machine learning, has taken the technological world by storm, underpinning the advancements in various applications from autonomous vehicles to drug discovery. Three dominant paradigms within deep learning are supervised, unsupervised, and self-supervised learning. In this article, we will elucidate these methods, noting their similarities and distinctions.