jonnor/embeddedml
Notes on Machine Learning on edge for embedded/sensor/IoT uses
This project helps engineers and product developers embed machine learning capabilities directly into small, low-power devices like sensors and microcontrollers. Instead of sending all raw data to the cloud, it enables your device to process sensor inputs (like audio, vibration, or accelerometer data) on-site and output immediate, intelligent decisions or filtered information. This is for professionals building smart devices for applications like predictive maintenance, activity tracking, or gesture recognition.
304 stars.
Use this if you need to run machine learning models directly on embedded systems or microcontrollers to enable local decision-making, ensure data privacy, reduce data transmission, or operate in environments with unreliable connectivity.
Not ideal if your application requires complex, large-scale deep learning models that need significant computational power and can tolerate cloud-based processing and data transfer.
Stars
304
Forks
37
Language
Jupyter Notebook
License
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Category
Last pushed
Feb 23, 2026
Commits (30d)
0
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