Forget weeks or days. You can now start your embedded machine learning journey in minutes!
Join Jenny Pluckett for an Introduction to TinyML using Edge Impulse. Edge Impulse enables developers to create the next generation of intelligent device solutions with embedded Machine Learning. Machine Learning at the very edge will enable valuable use of the 99% of sensor data that is discarded today due to cost, bandwidth, or power constraints. Edge Impulse enables the easy collection of real sensor data, live signal processing from raw data to neural networks, testing and deployment to any target device. You can sign up for a free developer account and get started with the ST IoT Discovery board or the Arduino Nano Sense 33. Their open source SDKs allow you to collect data from or deploy code to any device. TinyML enables exciting applications on extremely low-power MCUs. For example, you can detect human motion from just 10 minutes of training data, detect human keywords and classify audio patterns from the environment in real-time.
The presentation will include a live demo using the Arduino Nano 33 for ML in minutes, not days! This will be followed by a Q&A session.
TinyML is opening up incredible new applications for sensors on embedded devices, from predictive maintenance to health applications using vibration, audio, biosignals and much more! This webinar introduces why ML is useful to unleash meaningful information from that data, how this works in practice from signal processing to neural networks, and walks the audience through hands-on examples of gesture and audio recognition using Edge Impulse.
What you will learn by attending:
- Overview of embedded design and the role of TinyML in neural networks
- Introduction to the Edge Impulse Studio and TinyML development
- Live demo using the Arduino Nano 33 BLE - ML in minutes, not days!
- Q&A
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Prizes:
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| Buy NowBuy Now | |
| Simply register for the webinar for a chance to win an Oura Ring. One attendee will be chosen at random to receive an Ora Ring! | We will be sending out a limited number of Arduino Nano Sense 33 boards for asking the best questions during the presentation! |
The Presenter:
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| Jenny Plunkett, User Success Engineer at Edge Impulse | Daniel Situnayake, Founding TinyML Engineer at Edge Impulse |
| Texas Longhorn and software engineer, working as a User Success Engineer at Edge Impulse. Since graduating from The University of Texas I have been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for the Pelion platform. | I'm a founder, an engineer, a teacher and a communicator. I currently work at Edge Impulse as Founding TinyML Engineer. I'm a co-author of the book "TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers", published by O'Reilly.
I previously worked at Google as a Developer Advocate for TensorFlow Lite, enabling developers to deploy machine learning to edge devices, from phones to SoCs. I was also Developer Advocate for Dialogflow, a tool for building conversational AI. |
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