Practical test: First ML experiments with Google Coral and TensorFlow Lite. Some Raspberry Pi projects use machine learning to recognize objects, speech or gestures or to process data using neural networks. If you want to run “AI applications” in the form of deep or machine learning (DL, ML) in the IoT area, for example on an old laptop, a Raspberry Pi or other “single board computers” (SBC), you come across but quickly to the limits of computing power. Face or object recognition via a camera, for example, runs in slow motion on the Raspi. This may be enough for experiments in this area and a “proof of concept”, but not for practical use.