Embedded AI has become a relevant research line with real applications involving the deployment of AI algorithms in units with limited computing resources, such as microcontrollers (MCUs). Embedded AI has led to TinyML, a recently emerged paradigm that proposes to embed optimized ML models in MCUs. Traditionally, this deployment has required a deep knowledge of low-level programming, but currently some higher-level software libraries ease this deployment. However, analysis and practical examples of these libraries are still scarce. The main objective of this paper is to present the practical deployment of some of these libraries on a Proof of Concept and a test bench to analyze their effects on the inference latency and accuracy of the AI models. Results show that the development and deployment of these embedded models is already a feasible task requiring only basic AI and programming experience. Furthermore, the inference latency and accuracy of these models meets the requirements of several real-world applications.