Park Smart is a solution to lead drivers to find free parking spaces, and help parking owners and managers to improve their business. We exploit the paradigm of Edge Computing, moving the computational load from servers in the Cloud to embedded devices located in place. Such a solution dramatically reduces the bandwidth consumption by ~95%. We perform the fine-tuning of a pre-trained CNN model able to classify empty vs. non empty parking lots using the NVIDIA Jetson inside our AISee box, and then we stream the result to the Cloud as a JSON file. A DL pipeline allows us to have a more robust classification with respect to classical CV techniques. We will present our end-to-end architecture together with the results of the benchmark tests about fine-tuning and classification on TXn.