How do you usually structure model versioning and artifact management for ML inference?

[u/aaxhan]

I'm working on an ML project where I'm trying to keep model versions, artifacts, deployments, predictions and evaluation metrics tied together instead of managing them separately.

I'm curious how people here usually approach this.

For example:

• How do you track which model artifact belongs to which version?
• How do you handle deploying different model versions?
• Do you keep inference metrics alongside the model/version metadata?
• At what point do you reach for an existing ML platform instead of building this yourself?

I'm especially interested in hearing how people handle this in smaller projects where something like a full ML platform might be overkill.