I agree that pickle works well for storing arbitrary metadata, but my main gripe is that it isn't like there's an exact standard for how the metadata should be formatted. For FITS, for example, there are keywords for metadata such as the row order, CFA matrices, etc. that all FITS processing and displaying programs need to follow to properly read the image. So to make working with multi-spectral data easier, it'd definitely be helpful to have a standard set of keywords and encoding format.
It would be interesting to see if photo editing software will pick up multichannel JPEG. As of right now there are very few sources of multi-spectral imagery for consumers, so I'm not sure what the target use case would be though. The closest thing I can think of is narrowband imaging in astrophotography, but normally you process those in dedicated astronomy software (i.e. Siril, PixInsight), though you can also re-combine different wavelengths in traditional image editors.
I'll also add that HDF5 and Zarr are good options to store arrays in Python if standardized metadata isn't a big deal. Both of them have the benefit of user-specified chunk sizes, so they work well for tasks like ML where you may have random accesses.
I disagree. Scaling might seem trivial now, but the state-of-the-art architectures for NLP a decade ago (LSTMs) would not be able to scale to the degree that our current methods can. Designing new architectures to better perform on GPUs (such as Attention and Mamba) is a legitimate advancement. Furthermore, the viability of this level of scaling wasn't really understood for a while until phenomenon like double descent (in which test error surprisingly goes down, rather than up, after increasing model complexity past a certain degree) were discovered.
Furthermore, lots of advancements were necessary to train deep networks at all. Better optimizers like Adam instead of pure SGD, tricks like residual layers, batch normalization etc. were all necessary to allow scaling even small ConvNets up to work around issues such as vanishing gradients, covariate shift, etc. that tend to appear when naively training deep networks.