emlearn and emlearn-micropython
These are ecosystem siblings where emlearn-micropython provides a MicroPython-specific binding and runtime for the core inference engine and DSP capabilities defined in emlearn, allowing the same ML models to run on resource-constrained microcontrollers through different language ecosystems.
About emlearn
emlearn/emlearn
Machine Learning inference engine for Microcontrollers and Embedded devices
This tool helps embedded systems engineers and product developers deploy machine learning models directly onto low-power microcontrollers. You can train common classification, regression, or anomaly detection models using Python libraries like scikit-learn or Keras, and then convert them into highly optimized C code. This allows your device to make intelligent decisions on sensor data or other inputs without needing a connection to a more powerful computer.
About emlearn-micropython
emlearn/emlearn-micropython
Machine Learning and Digital Signal Processing for MicroPython
This project helps embedded systems developers integrate machine learning and digital signal processing into their MicroPython applications without writing C code. It takes your pre-trained machine learning models (like decision trees or CNNs) and sensor data, then efficiently runs predictions or filters on low-power microcontrollers. Embedded systems developers working with MicroPython to build smart devices will find this useful.
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