devolearn: long term vision

This issue would be a tracker/conversation space for devolearn's core long term goals.

Here are some of the key goals to work on:
* every model should be a subclass of [`devolearn.base_inference_engine.InferenceEngine`](https://github.com/DevoLearn/devolearn/blob/master/devolearn/base_inference_engine.py)
* [`InferenceEngine`](https://github.com/DevoLearn/devolearn/blob/master/devolearn/base_inference_engine.py) would contain some predefined (but overrideable) methods to:
* Download model checkpoints from github source
* Preprocess images for inference
* Postprocess predictions for readability/saving
* `devolearn.IoPipeline` would contain some standardized media tools for reading and writng videos/images (might use decord/imageio for videos and cv2 for images)
* a docker based fastAPI endpoint could be created (only after the above objectives are complete). This would help us deploy the models into servers/webapps/GUIs

The picture attached below is the layout of how we want to see it :arrow_down:


If you have any suggestions/ideas, feel free to discuss below :)

cc: @balicea @ujjwalll

Inizia leggendo devolearn/base_inference_engine.py e individuando gli attuali punti di ingresso di model e IoPipeline. Confronta la struttura esistente con gli obiettivi elencati, quindi suddividi la visione in issue di implementazione con ambito definito. Per considerarlo completato, sono necessarie milestone definite per l’ereditarietà del modello, la gestione dei media e il deployment, anziché un’unica discussione architetturale.

Scritto dal modello di indicizzazione a partire dal testo della issue.