One of the maintainers here. I published this link tbh to specifically emphasize the experiment management aspect of DVC. Historically because of its name (Data Version Control) users perceived it as a pure replacement for LFS scenarios, while in reality it always had pipelines, metrics, etc, etc.

I 100% agree that managing large datasets by moving them around is not practical, and definitely not in LFS/DVC-style. There should be a level of indirection if reproducibility is needed (pointers are versioned to files, not the data directly, data should be staying in the cloud).

Here, I would love to one more time mention some other cool features that DVC has. E.g. `dvc exp` set of commands where it is creating custom git refs to snapshot experiments, of DVCLive logger that helps capturing metrics, plots, etc. And also VS Code extension [1] that provides quite cool experience for experiments workflow inside VS Code.

Point here is that for DVC the ability to capture some large files and directories (that do not fit into Git) was always a low level mechanism to support higher level scenarios (e.g. you need to save a model somewhere as an output of an experiment).

[1] https://marketplace.visualstudio.com/items?itemName=Iterativ...