My main line if thought was that I built basically your product for spotting when a cat climbed into my plant pots using ML and a RPi3 - the idea was that when it saw the cat, it would squirt a water pistol at it to scare it away - inference on the RPi 3 was too slow (if I was doing this now I'd use a coral accelerator maybe) and by the time it realised a cat had got into the plant pots, the cat had already taken a shit and left. I worry that your product might suffer from similar end to end latency. Niche use-case? Perhaps. I have Amazon Blink cameras here and the IFTTT integration is delayed by about 30 seconds so by the time you get a notification there is someone at your door it is to late to do anything as they will.have already left/kicked the door in by then etc. Doing all this locally would be super fast

My main concern was not really about privacy - you'll need to cover GRPR if someone from the EU happens to walk into frame of one of your customers' cameras one day in the future anyway (Good luck)

If you want to sell me an ML-based system, you’re welcome to train the models in the cloud, but they have to run on local-only assets. And you have to give me complete control over downloading new models periodically to a machine of my choice, and the updating my local devices.