Lack of Cuda is not a problem if for most ML frameworks. For example, in PyTorch you just tell it to use the “mps” (metal performance shaders) device instead of the “cuda” device.
That simply isn't true in practice. Maybe for inference, but even then you're running up against common CUDA kernels such as FlashAttention which will be far from plug and play with PyTorch.
I tried training some models using tensorflow-metal a year ago and I was quite disappointed. Using a relu activation function led to very poor accuracy [0] and training time was an order of magnitude slower than just using the free tier of Google Colab
