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

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.