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150Gb ML model file? That's cool!

Yes, DVC can help with that. Where the data lives? S3/GCS or just a server with SSH?

Disclaimer: I'm a creator of DVC.

They live in an Azure Storage Account (Azure's not-S3). They're mounted as a network storage into pods in our kube cluster. All we need to do to deploy is copy the data into `/<thing>/<timestamp>` and the code finds the newest version and loads it up. So really I'm just looking for a way to abstract azure blob storage from my ML people and to allow them to do the equivalent of `docker tag...` to choose what we roll out.

I think DVC (+CML) is a good solution for this. It "wraps" artifacts that you store into Git. And Git repo abstracts access to the cloud. In your case of the mounted storage it will look like `git pull` + `dvc checkout` after model is "merged" in the production/master branch.

CML can automate and make the process of preparing the model to be merged into that branch reliable, visible, robust, etc.

I'm happy to help with this flow, ping me on Twitter - @shcheklein in DM or ivan on DVC Discord.

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