Sure, multiple of our customers that distribute applications with a machine learning/AI component also need to distribute their models. They can use our OCI registry to distribute large images with huge layers. We specifically reworked our registry implementation to storing in-transit blobs on disk to save memory, ensuring the application doesn’t run out of memory [1].
Is registry OOM protection the only advantage your registry has for large layers? Robotics has a need for Docker tooling that handles large layers/images gracefully. Even if you've done the "right" thing and sideloaded your ML models with some other management system, CUDA layers and such are gigantic.
Edit: looking at this, this is very adjacent to some problems w/ robotics deployments. Fleet management, edge deployment, key management. Neat.
I'd be curious about the multi-artifact support. Can I declare a manifest that binds together multiple services (or a service and an ML model?) Do you support ML models as an artifact?
It has actually been scientifically proven otherwise in crowd theory : with the right setup, the crowd is more effective to take a good decision that the top1 best decision maker.
Exemple : a crowd playing chess may beat the top1 chess player, even though the crowd individually cannot beat him.