---
title: "DVC | Skills We Assign For | Azendo"
description: "DVC for data versioning — reproducibility, pipelines, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/dvc/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# DVC.

DVC versions datasets and models alongside code in Git. Large files are stored in remote object storage while lightweight pointers are committed, so a checkout can retrieve the exact data a commit was built against.

## Where DVC fits on a long engagement.

Reproducibility in machine learning requires versioning data as well as code, and Git alone cannot hold multi-gigabyte files. DVC resolves that by committing content-addressed pointers and keeping the data in object storage, so checking out an old commit retrieves the data that commit used.

Its pipeline definitions add dependency tracking, so a stage reruns only when its inputs change. On expensive training pipelines that avoids recomputing stages whose inputs are identical, which is a substantial saving in both time and compute.

## What an assigned team does with DVC.

Storage discipline is required. Every version of every dataset accumulates in remote storage, and without a retention policy the cost grows steadily for data nobody will ever check out again.

Deciding what to keep and for how long is ongoing housekeeping rather than a setup step, held within an agreed [committed monthly capacity](https://azendo.co/pricing/).

## What we use DVC for.

* Data retrievable per commit Checking out an old commit and getting the data it was built against.
* Stages rerun only when inputs change Dependency tracking that avoids recomputing expensive steps unnecessarily.
* Storage retention managed A policy for old versions, because they accumulate indefinitely otherwise.

## How DVC capacity is assigned.

Data versioning is assigned inside AI capacity, with a storage retention policy agreed at setup rather than after the bill grows.

## Roles we assign DVC for

* [ML Platform Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/ml-platform-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [MLOps handoff — skill we assign for](https://azendo.co/skills/mlops-handoff/)
* [ONNX — skill we assign for](https://azendo.co/skills/onnx/)
* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)

## Tell us what your roadmap needs DVC for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)