[4] mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices.

Its C++ codebase allows for seamless integration with sensors, facilitating direct data extraction and on-device preprocessing at the Edge.

This makes it suitable for low resource devices, as it requires only the ensmallen and Armadillo or Bandicoot depending on the type of hardware we are planning to deploy to.

In terms of binary size, mlpack methods have a significantly smaller footprint compared to other popular libraries.

Our objective is to simplify for the user the API and the main machine learning functions such as Classify and Predict.

The first one is Armadillo code and it is running on the CPU, while the second one can runs on OpenCL supported GPU or NVIDIA GPU (with CUDA backend)ensmallen[7] is a high quality C++ library for non linear numerical optimizer, it uses Armadillo or bandicoot for linear algebra and it is used by mlpack to provide optimizer for training machine learning algorithms.

mlpack is fiscally sponsored and supported by NumFOCUS, Consider making a tax-deductible donation to help the developers of the project.

In addition mlpack team participates each year Google Summer of Code program and mentors several students.