{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"DVC is a command-line tool for versioning data and models alongside code, running reproducible machine learning pipelines, and tracking experiments locally using Git.","skillfed_tags":["ml-workflow","data-versioning","experiment-tracking"],"use_cases":["Version and share large datasets and trained models with team members using existing Git hosting (GitHub, GitLab)","Build reproducible ML pipelines that automatically track which steps need to re-run when code or data changes","Run and compare multiple experiments locally, filtering results by hyperparameters and metrics without external servers","Integrate data pipelines with CI/CD workflows to automatically reproduce experiments on code changes","Store data in cloud storage (S3, Azure, GCS) while keeping version metadata in Git for cost-effective large-scale projects"],"what_it_does":"DVC is a version control system for data and machine learning models that integrates with Git. It lets you store data artifacts and models outside your repository while keeping metadata in Git, similar to Git-LFS but without requiring a server. You define reproducible pipelines (computational graphs) that specify how to build models from code, data, and commands, then run only the steps affected by your changes.\n\nThe tool supports local experiment tracking\u2014you can prepare and run many experiments, compare their results by hyperparameters and metrics, and visualize performance plots. It works with multiple remote storage backends (S3, Azure, Google Cloud, SSH, etc.) for sharing and backing up your data cache. The package has 42 runtime dependencies including celery, networkx, hydra-core, and fsspec, enabling distributed task execution and flexible storage integration.","worth_installing":"Yes. DVC is actively maintained, has low install friction, and solves a real problem for ML teams managing data versioning and reproducible pipelines. The Apache-2.0 license is permissive. No known vulnerabilities. The large dependency tree (42 packages) is justified by its feature set, and optional storage backends let you add only what you need. Suitable for both individual data scientists and collaborative teams."},"id":"dvc","links":{"html":"https://skillfed.io/packages/dvc","md":"https://skillfed.io/packages/dvc.md","pypi":"https://pypi.org/project/dvc/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-31","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"dvc","python_support":"supports_current","summary":"Git for data scientists - manage your code and data together"},"popularity":{"monthly_downloads":2627704,"position":2964,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.67.1"}