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].

[1] https://github.com/distr-sh/distr/pull/1478

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?

Man. I absolutely hate that the term AI just has to be meaninglessly sprinkled over every single piece of marketing material now.

I don't even hate the player, it's just what you gotta do to run a business now. But man I can't wait for this chapter of the game to be over.

I feel you, but a huge percentage of recently funded companies are in the AI space. Software distribution for them is even more complex due to all the moving parts, and we want to make sure these companies know that our solution is a great fit for them.