Machine learning has developed considerably in the last decade and is penetrating all areas of information technology. Today, most computer systems use machine learning in one way or another. In addition, machine learning on less powerful devices has advanced greatly. The aim of the diploma thesis is to test the effectiveness of existing machine learning tools on less powerful devices. We focused on ARM devices. We built several different models on a personal computer in different frameworks to build machine learning models. We serialized the models using serialization tools and eventually ran them on a Raspberry Pi. We built several classification and one regression model. We measured the performance of the models and the time that the model spends on a particular device to predict. The results showed that the performance of the models on different devices did not differ. The difference in measured time, however, varied considerably between devices.