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

Beginne mit dem Lesen von devolearn/base_inference_engine.py und ermittle die Einstiegspunkte für das aktuelle Modell und IoPipeline. Vergleiche die bestehende Struktur mit den aufgeführten Zielen und teile die Vision anschließend in klar abgegrenzte Implementierungs-Issues auf. Für den Abschluss sind definierte Meilensteine für Modellvererbung, Medienverarbeitung und Deployment erforderlich, statt einer einzelnen Architekturdiskussion.

Vom Indexierungsmodell aus dem Issue-Text verfasst.