I think you're right and you're wrong, it's a bit more complicated.

ML is one of the few applications that benefit from platform-specific optimizations, so if you need every ounce of performance, you have your choice of which walled garden you want to tether your application to. The "lock-in" comes from the specific capabilities of your special-purpose hardware, and for serious applications, you're already thinking hard about whether to design your entire implementation around Apple, NVidia, Google/TPU, or even Android devices. For big models, platform-specific needs influence every aspect of model design, including data/model sharding, quantization, training loops...

For non-scientific applications, it's usual practice to train your model in platform-agnostic ways using PyTorch or Tensorflow or whatever and then deploy it to devices in platform-specific ways, whether that's XLA, CoreML, Edge TPU, Android NNAPI, TensorflowJS, or hell, custom-written GLSL shaders or whatever.

We're just starting to see cross-platform frameworks that abstract model inference: TFLite, PyTorch Mobile, ONNX. To their credit, CoreML can act as a backend for any of these, so you don't even need to worry about your platform.

Every platform is a golden cage in some respect. Ask any business who is stuck on ancient Win32 and even DOS applications, source code long gone. (Looking at you my local McDonalds, Menards, Tractor Supply)…

The worst thing is, their users don’t even seem to be totally happy with the state of affairs! It’s like they don’t even realize their preferences are wrong. :(

This was intended to be obvious sarcasm, but I somehow accidentally added “don’t” which… really just makes it confusing. Oops, haha.