Arduino Tiny Machine Learning Kit
Ever wondered how to build a small intelligent device that reacts to sounds like a keyword being spoken, recognizes gestures like waving a magic wand, or even recognize faces? With this kit combined with the power of Tiny Machine Learning (TinyML) you can do all of that and much more! We want to show you how these possibilities can be part of your own tiny smart device!
Overview
The Tiny Machine Learning Kit, combined with the exciting TinyML Applications and Deploying TinyML on Microcontrollers courses that are part of the Tiny Machine Learning (TinyML) specialization from EdX will equip you with all the tools you need to bring your ML visions to life!
The kit consists of a powerful board equipped with a microcontroller and a wide variety of sensors (Arduino Nano 33 BLE Sense*). The board can sense movement, acceleration, rotation, barometric pressure, sounds, gestures, proximity, color, and light intensity. The kit also includes a camera module (OV7675) and custom Arduino shield to make it easy to attach your components and create your very own unique TinyML project. You will be able to explore practical ML use cases using classical algorithms as well as deep neural networks powered by TensorFlow Lite Micro. The possibilities are limited only by your imagination!
“The Future of Machine Learning is Tiny and Bright. We’re excited to see what you’ll do!”
Prof. Vijay Janapa Reddi, Harvard University and Pete Warden, Google
*For us to be able to have this kit back in stock we produced a Nano 33 BLE Sense without the HTS221 sensor (temperature and humidity), this change does not affect this kit’s usage and/or content experience. This board is fully compatible with the kit’s documentation.
Tech specs
The Tiny Machine Learning Kit includes:
- 1 Arduino Nano 33 BLE Sense board
- 1 OV7675 Camera
- 1 Arduino Tiny Machine Learning Shield
- 1 USB A to Micro USB Cable
Conformities
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
Get Inspired
Using the Garmin LIDARLite v3HP, Arduino MKR WIFI 1010 and Pushsafer to detect an intruder and send a push notification to a smartphone.
Being able to monitor the weather in real-time is great for education, research, or simply to analyze how the local climate changes over time. This project by Hackster.io user Pradeep explores how he was able to design a simple station outdoors that could communicate with a cloud-based platform for aggregating the sensed data. The board Pradeep selected is the Arduino MKR WiFi 1010 owing to its low-power SAM D21 microcontroller and Wi-Fi/BLE connectivity for easy, wireless communication. After configured, he connected a DFRobot Lark Weather Station, which contains sensors for measuring wind speed/direction, temperature, humidity, and barometric pressure — all in a compact device. Every second, the MKR WiFi 1010’s sketch polls the sensors for new data over I2C before printing it to USB. The cloud integration aspect was achieved by leveraging Qubitro’s platform to collect and store the data for later visualization and analysis. To set it up, Pradeep created a new device connection and copied the resulting MQTT endpoint/token into his sketch. Then once new data became ready, it got serialized into a JSON payload and sent to the topic where a variety of widgets could then show dials and charts of each weather-related metric. To read more about this DIY weather station, you can visit Pradeep’s project write-up here.