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Home/Tasks/DVC (Data Version Control)
DVC (Data Version Control) logo

DVC (Data Version Control)

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Quick Tool Decision

Should you use DVC (Data Version Control)?

DVC brings software engineering best practices to data, AI/ML, and data science teams using a Git-like model for data version control.

Category

Data & ML

Data confidence: release and verification fields are source-audited when available; other summary fields are community-aggregated.

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Overview

DVC (Data Version Control) is an open-source version control system for machine learning projects. It extends Git to handle large files, datasets, machine learning models, and metrics, enabling data scientists and machine learning engineers to version their data alongside their code. DVC allows users to track changes to data, reproduce experiments, and collaborate effectively on data science projects. It focuses on data versioning, experiment management, and reproducibility, making it easier to manage complex ML workflows. By integrating seamlessly with Git, DVC provides a familiar interface and workflow for data version control, making it accessible to both individual data scientists and enterprise AI teams.

Common tasks

Version large datasets and ML modelsTrack changes to data and code togetherReproduce experiments and pipelinesCollaborate on data science projectsManage data dependenciesAutomate ML workflowsCompare experiment results

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Pricing

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