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Computer science and engineering have evolved rapidly over the last decade offering innovative Machine Learning frameworks and high-performance hardware devices. Executing data analytics at the edge promises to transform the mobile computing paradigm by bringing intelligence next to the end user. However, it remains an open question to explore if, and to what extent, today’s Edge-class devices can support ML frameworks and which is the best configuration for efficient task execution. This paper provides a comparative evaluation of Machine Learning inference machines on Edge-class compute engines. The testbed consists of two hardware compute engines (i.e., CPU-based Raspberry Pi 4 and Google Edge TPU accelerator) and two inference machines (i.e., TensorFlow-Lite and Arm NN). Through an extensive set of experiments in our bespoke testbed, we compared three setups using TensorFlow …