The integration of machine learning on microcontrollers at the extreme edge of the network is gaining significance for various Internet of Things applications. This paradigm shift enables real-time data processing and decisionmaking directly on the devices, thereby reducing latency and reliance on cloud connectivity. Nevertheless, due to the computational and memory limitations of microcontrollers, training and deploying a neural network using existing libraries such as TinyML remains impractical for some devices. This paper proposes an approach where the model is trained in the cloud, with only the forward phase directly implemented on the microcontroller. We leverage the LittleBits platform
for its user-friendly interface and suitability for educational purposes, aiming to introduce machine learning concepts to non-expert users. Our case study focuses on temperature prediction in smart greenhouses, demonstrating the practical utility of machine learning in timely interventions for plant disease prevention and optimal growth conditions. This work not only illustrates the feasibility of deploying machine learning on resource-constrained devices but also emphasizes the potential of LittleBits in making novel technologies accessible and comprehensible to a broader audience.