The concept of “digital transformation” sounds both intimidating and expensive for most organizations. Replacing legacy analog systems hardware and software to achieve a modernization utopia, while a lofty goal, is unrealistic for most companies. So how do we monitor legacy analog systems without the feat of complete digital transformation?
Today we can measure, monitor, and take action on closed analog systems without having to initiate pricey upgrades and systems changes. While in the past this would have been unattainable we’ve found ways to make this work effectively and efficiently, but how?
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. Giving companies the advantage of updating without the unrealistic goal of replacing entire systems. With these techniques, we will move companies into the digital age without the need for a complete system replacement.
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.