In this chapter, we looked at the power of combining edge analytics with machine learning. With edge analytics, the latency and reliability of machine learning algorithms are much improved. We looked at how pushing machine learning processing onto the edge improves an application such as an automated security door application, where having a reduced latency is critical.

We then did a practical vision recognition example where our program was able to distinguish a human face from the face of a dog. We looked at the power of the current crop of camera-based microcontrollers by programming it to decode the payload on a QR code. We finished this chapter by taking a brief look at what Microsoft offers for machine learning on the edge.

What you do with the knowledge gained from this chapter is up to you. Maybe you own or know someone who owns a small security firm and may be able to use this knowledge to alert your clients when a person is at the back door. Perhaps you run a small hotel and, as suggested previously, would like to cut down on the cost of keys.

In our next chapter, we will take a lot of what we learned in this chapter and create a smart doorbell using visual recognition.