  In this paper we outline the development methodology for an automatic dog
treat dispenser which combines machine learning and embedded hardware to
identify and reward dog behaviors in real-time. Using machine learning
techniques for training an image classification model we identify three
behaviors of our canine companions: "sit", "stand", and "lie down" with up to
92% test accuracy and 39 frames per second. We evaluate a variety of neural
network architectures, interpretability methods, model quantization and
optimization techniques to develop a model specifically for an NVIDIA Jetson
Nano. We detect the aforementioned behaviors in real-time and reinforce
positive actions by making inference on the Jetson Nano and transmitting a
signal to a servo motor to release rewards from a treat delivery apparatus.
