As all of the Arduino boards which can harness the ML model, also have the power to detect a whole host of other data via sensors, there is a good chance doing both is also useful!
e.g. A basic example:
A cooling setup whose aim is to keep cooling a warm environment to xx degrees, whereby the predictions are used to try and anticipate any actions to take, and the data is logged back to show what actions had to be taken.
The control system on the Arduino would run with the predictive data unless there were other alarms triggered which override its behaivour.
This can vastly reduce the time it takes to gather the data needed to make a reliable model.
In the example we use here we are essentially just breaking up the hello_world example, so that the ESP32 will send the data back to the Model builder, for the sin() function (with noise added).
It will also be using the previous upload of the model to flash its red LED at the predicted brightness (we eventually get a smooth curve as in the example test model).
(Note: there is also additional code present for OTA Uploads from the Examples section)
Download ESP32EdgeDevice Example Sketch for ML