Monitoring Legacy Analog Systems With TinyML, Edge Impulse, and Blues

There's a lot going on in that title, so we will slowly but surely unpack it all for you today. As the title says, we're going to look at what I consider one of the most engaging and useful use cases in the IoT. That is regarding machine learning, specifically TinyML, and how hardware and services from Edge Impulse and Blues work together – and dare I say, accelerate certain digital transformation initiatives. I know that's a very overused and loaded term but hopefully, you'll get a good sense of what we all mean by that today.

The concept of "digital transformation" sounds both intimidating and expensive for most organizations. Replacing legacy system hardware and software to achieve a modernization utopia, while a lofty goal, is unrealistic for most companies.

However, today we can measure, monitor, and take action on closed analog systems without having to initiate pricey upgrades and systems changes. Using established Machine Learning techniques alongside modern IoT technologies, we can effectively modernize by tracking and reporting on existing systems in previously unheard of ways.

In this webinar, we dive into "Monitoring Legacy Analog Systems with TinyML, Edge Impulse, and Blues Wireless". We spend our time together learning about:

1. What TinyML is and how it relates to the IoT.
2. The value of combining Edge Computing capabilities with cellular IoT.
3. Building an ML solution on constrained devices with Edge Impulse.
4. How two working projects use TinyML to monitor analog gauges and perform anomaly detection in thermal imagery.

Speakers:

Rob Lauer, Developer Relations at Blues Wireless

TJ VanToll, Principal Developer Advocate at Blues

Louis Moreau, Senior Developer Relations Engineer at Edge Impulse