Ask HN: How do your ML teams version datasets and models?

Git worked until we hit a few gigabytes. S3 scales super well but version control, documentation, and change management isn't great (we just did lots of "v1" or "vsep28_2023" names).

DVC felt very clunky (now I need git AND s3 AND dvc) by the team.

What best practices and patterns have you seen work or have you implemented yourself?