dvc init in your workspace will initialize a DVC
project, including the internal
.dvc/ directory. From there on, you
will create and manage different DVC metafiles (below), and populate the
cache with data artifacts as you work on your ML experiments.
.dvc files ("dot DVC files") are placeholders to track data files and
.dvcignore files (optional) contain a list of paths for DVC to ignore, which
can dramatically increase its operational performance.
These metafiles are typically versioned with Git, as DVC does not replace its distributed version control features, but rather extends on them.