I think you're onto something. A "pip install" for models. Perhaps "pip install" is sufficient and we just need to start using it.

Back in the days, when I started Python, I didn't use external libraries. It was all the included batteries and docs, and it was great to start.

I was thinking about a new high-level module in the standard, you would just (pseudo python code) :

  import ai.vision
  guesses = ai.vision.guess(open('myimage.png'))
  print(guesses)
  # ["cat","person","boat"]

Three lines, everything else is abstracted. You would have ai.vision and ai.text for a start, with some accessors to other state-of-the-art models (like ai.vision.imagenet if you know better what your are doing)

The underlying models would be shipped and updated with newer versions of Python.

Not exactly. The problem is that different models are tuned for different use cases. So I'd expect something like this:

   $ pip install pymodel-ResNet-1337

   #! /usr/bin/env python3
   import ai
   model = ai.loadModel("ResNet-1337",input=ai.input.Image,output=ai.output.Category)
   model.guess(open('myimage.png'))

Problems abound, for example, how would you resample/rescale the image.

I'd want, to start, a standard model format that can be serialized/deserialized into any language that can be (for example) pip installed and loaded. People seem to use HD5 but I don't think there is any sort of "standard".

So I'd expect the first incarnations of this idea to look like this:

   $ pip install tfmodel-ResNet-1377

or

   $ pip install kerasmodel-ResNet-1337

With some hooks for loading models:

   #!/usr/bin/env python3

   # whereas before you'd build a network, train it, and then use it, here you get the whole shebang in one go
   model = keras.loadInstalledModel("ResNet-1337")

Rest of it is up to the user, as usual

Could just use opencv and check for your face.

try yolo.