openskills.info
Course catalog
Data and Model Versioning with DVC logoCourse Preview

Data and Model Versioning with DVC

DVC keeps large datasets and model files outside Git while Git tracks small files that identify each exact version. This lets a project restore matching code, data, and models without putting large binaries in the Git repository.

itArtificial intelligence and machine learning

Don't Panic - Data and Model Versioning with DVC

DVC versions large data and model files beside Git without stuffing those binaries into Git history. Git stores small metadata: .dvc files, dvc.yaml, dvc.lock, and configuration. A local cache and an optional remote hold the actual bytes, addressed by content hash. That split is the whole product: Git remains good at text review, while object storage remains good at large files, and DVC connects the two without replacing either one.

The identity chain is short and strict. A Git revision selects metadata. That metadata names a content hash. The hash must resolve in a local cache or a DVC remote before the workspace path can be restored. Break any link after the Git revision and the artifact is not recoverable from that storage set. Identical content maps to the same object, so unchanged files can be reused across dataset versions. Rewriting a monolithic archive changes the archive hash even when most inner files are unchanged, which is why collections of stable files often reuse better than one rebuilt blob per revision.

The everyday workflow is two systems, not one. dvc init creates project files. dvc add hashes a path, writes metadata, and keeps the bytes out of Git. You commit that metadata with Git. dvc remote add records a shared destination. dvc push uploads cache objects. A teammate pulls Git first, then dvc pull to fetch the matching objects and materialize the workspace. A successful Git push never proves the objects are shared. Changing versions is the same dual motion in reverse: Git selects metadata, then dvc checkout or dvc pull restores the workspace.

Pipelines extend the story when you need them. dvc.yaml declares stages; dvc.lock pins realized hashes. A standalone .dvc file is enough when you only need an identity for an artifact. dvc status and dvc diff compare workspace and revisions at the path and hash level. They do not judge model quality. dvc gc frees unused objects and can delete the last copy of something an unexamined branch still names, so retention scope is part of the design.

Read the Intro for the three layers and failure modes. Use the Cheatsheet when you need the command responsibility map. Landscape and Timeline place DVC among related tooling; Updates tracks the release line that changes those commands.

Where this skill leads

Relevant careers

See how this topic contributes to broader role-level skill maps.

Sources