Edge ML on the Raspberry Pi: Remote Birding and Traffic Monitoring

The worlds of Machine Learning and Edge Computing are merging faster than anyone may have imagined even a couple of years ago. Today you can build custom ML models that are optimized to run on low-power microcontrollers or single-board computers like the Raspberry Pi. Plus, using a low-power cellular module like the Blues Wireless Notecard, you can securely route ML-derived inferences to your cloud of choice!

In this webinar, we walk through two silly, somewhat impractical, but super fun projects that test the limits of ML on the Raspberry Pi!

1) The "Remote Birding with TensorFlow Lite and Raspberry Pi" project will show us how to use ML on an RPi in a remote environment (complete with cellular connectivity and solar power!).

2) In "Busted! Create an ML-Powered Speed Trap" we will walk through building a portable "speed trap" that uses ML to identify vehicles, a radar sensor to measure speed, and the Notecard to report data to the cloud.

By the end of the webinar, you should have a basic understanding of some common image-related ML concepts and how to start implementing them in an edge computing scenario on the Raspberry Pi.