Tensorflow lite provides a smooth experience for training models in python and deploying on-device for inference on edge devices (e.g., raspberry pi, arduino, edge TPUs, smartphones etc). It is also well supported and actively developed.
The biggest challenge along this process lies with correctly transforming your input features captured on device (e.g. audio from a microphone, images from the camera, input from the UI etc.,) to the correct format that the tensorflow lite interpreter on Android expects. In this case, the model expects a 9 dimensional array of floats, so this is straightforward. Models with text data, audio or image data will be a bit more complex (manually converting that data to Float buffers).
Outro: I hope you are all doing well! Sending good vibes!