Machine Learning and Big Data on Raspberry Pi: A Performance Evaluation

In this paper we present a performance evaluation of machine learning and big data technologies in edge computing. Existing research focuses on deep learning methods on high-end edge computing devices. There have been few reports on classical machine learning methods on common edge computing devices. To close this gap, we evaluate support vector machine (SVM) and random forest (RF) on a Raspberry Pi platform. Our evaluation includes method execution time and data storage time. The latter is considered an important component in machine learning execution lifecycle and has not been jointly evaluated in existing research.