# SparkFun MicroMod Machine Learning Carrier Board

**Type:** Product page · **SKU:** DEV-16400 · **Brand:** [Sparkfun](https://core-electronics.com.au/brands/sparkfun-australia)
**Page:** https://core-electronics.com.au/sparkfun-micromod-machine-learning-carrier-board.html ([markdown](https://core-electronics.com.au/sparkfun-micromod-machine-learning-carrier-board.html.md))

The MicroMod Machine Learning Carrier Board combines some of the features of our SparkFun Edge Board and SparkFun Artemis boards, but allows you the freedom to explore...

## Pricing

- **Price:** $38.30 (inc GST) — $34.82 AUD, exc GST
- **Quantity discounts:** 5+ $33.77 (exc GST) · 25+ $33.07 (exc GST) · 100+ $31.68 (exc GST)

## Availability & dispatch

- Available with a lead time — expect dispatch between Aug 17 and Aug 24.

## Restrictions

Restrictions apply to this equipment as it uses button/coin cell batteries:

- The equipment is intended to be used in trades, professions or industries
- The equipment is not intended for sale to the general public
- The equipment is not intended to be used where children are present
- You are aware that this equipment may not meet child safety testing requirements as it is not intended to be used by consumers or general public, but rather, is intended to be used in trades, professions or industries in conjunction with the limitations and restrictions listed above
- You are aware that these restrictions are due to a change in [A](https://www.accc.gov.au/media-release/businesses-on-notice-as-mandatory-button-battery-laws-commence)[ustralian legislation in 2022](https://www.accc.gov.au/media-release/businesses-on-notice-as-mandatory-button-battery-laws-commence) for button/coin cell batteries
 
![Button Battery Warning](https://core-electronics.com.au/attachments/uploads/iec-button-battery.png)

A declaration of use will need to be submitted during checkout.

## Description

The MicroMod Machine Learning Carrier Board combines some of the features of our SparkFun Edge Board and SparkFun Artemis boards, but allows you the freedom to explore with any processor in the MicroMod lineup without the need for a central computer or web connection. Voice recognition, always-on voice commands, gesture, or image recognition are possible with TensorFlow applications. The cloud is impressively powerful but all-the-time connection requires power and connectivity that may not be available. Edge computing handles discrete tasks such as determining if someone said "yes" and responds accordingly. The audio analysis is done on the MicroMod combination rather than on the web. This dramatically reduces costs and complexity while limiting potential data privacy leaks.

This board features two MEMS microphones (one with a PDM interface, one with an I2S interface), an ST LIS2DH12 3-axis accelerometer, a connector to interface to a camera (sold separately), and a Qwiic connector. A modern USB-C connector makes programming easy and we've exposed the JTAG connector for more advanced users who prefer to use the power and speed of professional tools. We've even added a convenient jumper to measure current consumption for low power testing.

*MicroMod is a modular interface ecosystem that connects a microcontroller “processor board” to various “carrier board” peripherals. Utilizing the M.2 standard, the MicroMod standard is designed to easily swap out processors on the fly. Pair a specialized carrier board for the project you need with your choice of compatible processor!*

 [Get Started With the MicroMod Machine Learning Carrier Board Guide](https://learn.sparkfun.com/tutorials/micromod-machine-learning-carrier-board-hookup-guide)**Features**

- M.2 MicroMod Keyed-E H4.2mm 65 pin SMD Connector 0.5mm
- Digital I2C MEMS Microphone PDM Invensense ICS-43434 (COMP)
- Digital PDM MEMS Microphone PDM Knowles SPH0641LM4H-1 (IC)
- ML414H-IV01E Lithium Battery for RTC
- ST LIS2DH12TR Accelerometer (3-axis, ultra-low-power)
- 24 Pin 0.5mm FPC Connector (Himax camera connector)
- USB - C
- Qwiic connector
- MicroSD socket
- Phillips #0 M2.5x3mm screw included

**Documents**

**MicroMod Machine Learning Carrier Documentation:**

- [Schematic](https://core-electronics.com.au/attachments/localcontent/16400_SparkFun_MicroMod_Machine_Learning_Carrier_Board_Schematic_775313b3378.pdf)
- [Eagle Files](https://core-electronics.com.au/attachments/localcontent/16400_SparkFun_MicroMod_Machine_Learning_Carrier_Board_EagleFiles_23188132ea1.zip)
- [Hookup Guide](https://learn.sparkfun.com/tutorials/micromod-machine-learning-carrier-board-hookup-guide)
- [Board Dimensions](https://core-electronics.com.au/attachments/localcontent/Machine_Learning_Carrier_dimensions_4990033aa03.png)
- [GitHub Hardware Repo](https://github.com/sparkfun/MicroMod_Machine_Learning_Carrier)
 
**MicroMod Documentation:**

- [Getting Started with MicroMod](https://learn.sparkfun.com/tutorials/getting-started-with-micromod)
- [Designing with MicroMod](https://learn.sparkfun.com/tutorials/designing-with-micromod)
- [MicroMod Info Page](https://www.sparkfun.com/micromod)
- [MicroMod Forums](https://forum.sparkfun.com/viewforum.php?f=180)

## Images

- [Product image 1](https://core-electronics.com.au/media/catalog/product/1/6/16400-sparkfun_micromod_machine_learning_carrier_board-01.jpg)