# Tiny Machine Learning with TensorFlow Lite

**URL:** <https://forum.digikey.com/t/tiny-machine-learning-with-tensorflow-lite/41684>\
**Category:** Maker, DIY\
**Tags:** adafruit-industries-llc, maker-diy, educational-kits, maker-diy-educational, development-boards-kits-programmers\
**Created:** [May 27, 2024, 7:01am UTC](https://forum.digikey.com/t/tiny-machine-learning-with-tensorflow-lite/41684 "2024-05-27T07:01:00Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Barley\_Li](https://sea2.discourse-cdn.com/digikey/user_avatar/forum.digikey.com/barley_li/32/558_2.png) [@Barley\_Li](https://forum.digikey.com/u/Barley_Li)\
**Post date:** [May 27, 2024, 7:01am UTC](https://forum.digikey.com/t/tiny-machine-learning-with-tensorflow-lite/41684/1 "2024-05-27T07:01:00Z")

</div>

Now it does not need a complicated hardware to design your own TensorFlow model. The following simple and very tiny version of kit, [TensorFlow Lite](https://www.digikey.com/en/products/detail/adafruit-industries-llc/4317/10313913) from [Adafruit](https://www.digikey.com/en/supplier-centers/adafruit) can perform Machine Learning computations.

 ![image](https://global.discourse-cdn.com/digikey/original/3X/6/b/6bd92fd5d204670432393fa0b3ff31f655db3b1e.jpeg)  
TensorFlow Lite kit

This kit uses [Adafruit PyBadge board](https://www.digikey.com/en/products/detail/adafruit-industries-llc/4200/10130491) (SAMD51 Cortex M4F prcoessor@120MHz) as your edge processor. Its size is about the size of a credit card. It’s powered by ATSAMD51, with 512KB of flash memory and 192KB of RAM. It also features 2 MB of QSPI flash memory for file storage, making it easy to store TensorFlow Lite files, images, fonts, sounds, or other assets.

 ![image](https://global.discourse-cdn.com/digikey/original/3X/9/4/9448edc6c12f6f7839481fc126408f303499dde8.jpeg)  
Adafruit PyBadge board

If desired, it can plug a microphone into the port on the bottom to add a microphone input for micro-speech recognition. TensorFlow Lite for microcontrollers is very cutting-edge, so expect a lot of development in this area, including a lot of code and process changes.

To learn more TensorFlow Lite, please take this [quickstart](https://cdn-learn.adafruit.com/downloads/pdf/tensorflow-lite-for-edgebadge-kit-quickstart.pdf)