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.yamlfiles define stages, parameters, metrics, and plots. Stages form the pipeline(s) of a project. Parameters, metrics, and plots are used to evaluate and compare project versions and may be defined within stages or independently.
.dvcfiles ("dot DVC files") are placeholders to track data files and directories.
.dvcignorefiles (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.