The AI Freezer Monitor is a DIY IoT monitor that uses machine learning (ML) to provide early warnings of potential equipment failure. This guide covers building the device, collecting training data, setting up email alerts, training a custom auto encoder machine learning model, and deploying the model to a Feather HUZZAH32 development board.

The project is designed to be functional for low temperature scientific freezers (-60 C), with the goal of reducing catastrophic failure and the need to keep backup freezers running full-time. However, please note that this project is primarily for demonstrative and educational purposes and has not gone through extensive testing.

This project takes about two to three hours to fully complete. But the device will need to passively collect temperature data for about 30 days before you will be able to train the machine learning model.


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This guide was first published on Jan 27, 2021. It was last updated on Jan 27, 2021.

This page (Overview) was last updated on Nov 20, 2021.

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