Yes, model weights saved with Keras Core are backend-agnostic. You can train a model in one backend and reload it in another.

Coral TPU could be used with Keras Core, but via the TensorFlow backend only.

Super cool, does that mean if someone trains something using the PyTorch backend, I can still use it with the coral if I load the weights using the tensorflow backend?

That's right, if the model is backend-agnostic you can train it with a PyTorch training loop and then reload it and use it with TF ecosystem tools, like serve it with TF-Serving or export it to Coral TPU.

How does the future look for TFLite and Edge AI/TinyML in general?

Will Keras Core support direct deployment to edge devices like RPi or Arduino?

Will the experience of defining and training a model in JAX/PyTorch and then deploying to edge devices be seamless?

Anything related on the roadmap?