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A TinyMLaaS Ecosystem for Machine Learning in IoT: Overview and Research Challenges

Tiny Machine Learning (TinyML) is an emerging concept that concerns the execution of ML tasks on very constrained IoT devices. Although TinyML has generated a strong R&D interest around it, various challenges limit its effective execution in the constrained devices world, with the result of slowing down the development of a complete ecosystem around it. TinyML as-a-Service (TinyMLaaS) aims to fill the gap in this respect, with the definition of a set of guidelines that can enable an easier democratization of TinyML. In this paper, we describe how the "as-a-Service" model is bound to TinyML, by providing an overview of our concept and introducing the design requirements and building blocks that can make TinyMLaaS reality.