Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.
Users have a highly positive view of DVC, with consistent high ratings that highlight its strengths in improving version control and collaboration for data science projects. Key strengths include ease of use and integration capabilities with existing workflows. There are very few complaints mentioned, indicating a generally satisfied user base. Pricing sentiment is not discussed in the reviews, but the overall reputation of DVC is very strong, with a notable presence and recognition on platforms like YouTube.
Mentions (30d)
0
Avg Rating
4.7
11 reviews
Platforms
1
GitHub Stars
15,568
1,292 forks
Users have a highly positive view of DVC, with consistent high ratings that highlight its strengths in improving version control and collaboration for data science projects. Key strengths include ease of use and integration capabilities with existing workflows. There are very few complaints mentioned, indicating a generally satisfied user base. Pricing sentiment is not discussed in the reviews, but the overall reputation of DVC is very strong, with a notable presence and recognition on platforms like YouTube.
Features
Use Cases
952
GitHub followers
131
GitHub repos
15,568
GitHub stars
20
npm packages
22
HuggingFace models
g2
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DVC uses a tiered pricing model. Visit their website for current pricing details.
DVC has an average rating of 4.7 out of 5 stars based on 11 reviews from G2, Capterra, and TrustRadius.
Key features include: track and save data and machine learning models the same way you capture code;, understand how datasets and ML artifacts were built in the first place;, adopt engineering tools and best practices in data science projects;, Subscribe for updates. We won't spam you..
DVC is commonly used for: Version control for machine learning models, Data versioning for reproducible research, Collaboration on data science projects, Tracking experiments and their results, Managing large datasets efficiently, Integrating with CI/CD pipelines for ML workflows.
DVC integrates with: GitHub, GitLab, Bitbucket, Azure DevOps, AWS S3, Google Cloud Storage, Azure Blob Storage, Kubernetes, MLflow, TensorFlow.
Pieter Levels
Founder at PhotoAI / NomadList
1 mention
DataChain Open-Source Release - A new way to manage your Unstructured Data
Jul 25, 2024
DVC has a public GitHub repository with 15,568 stars.
