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Machine Learning at the Edge on the Raspberry Pi - On Demand Webinar

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, if you use 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 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 you 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 will 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.

00:00 — Intro to Blues Wireless and Machine Learning

10:28 — Project Walkthrough and Demo- Remote birding with the Raspberry Pi

24:27 — Introducing Edge Impulse

35:32 — Project Walkthrough and Demo- Building a portable speed trap with Raspberry Pi

44:11 — Outro

Webinar Transcript - Machine Learning at the Edge on the Raspberry Pi

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