DVC Studio
Machine Learning Experiments based on Git
DVC Studio launched on June 11, 2021 with 95 votes, #510 of 3,650 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 5 years on.
What it does
Extend your GitHub, GitLab or BitBucket with metrics dashboard, graphs and the ability to run ML experiments in clouds (AWS, Azure, Kubernetes).
Collaboration for Machine Learning Teams. We are the company behind DVC and CML, open-source tools to streamline the workflow of data scientists.
Does a similar job
all alternatives →
DVC Extension for VS Code2023 · marketplace.visualstudio.com · ▲165Track machine learning experiments right in your IDE
- D1DVC 1.0 release. 5 lessons from 3 years of building open-source ML tool2020 · ▲19
Hey HN, creator of DVC here! DVC (https://dvc.org/) is known as Git for data projects. Technically, DVC codifies your data and machine learning pipelines as text metafiles (with pointers to actual data in S3/GCP/Azure/SSH) while you use Git for the actual versioning. DevOps folks call this approach GitOps or more specifically in this case - DataOps or MLOps. We’ve been working towards 1.0 since we started 3 years ago. What began as my pet project now has 100+ code contributors, 100+ documentation contributors, and thousands of users. Our community has taught us…
- WSWe scaled Git to support 1 TB repos2022 · xethub.com · ▲279
I’ve been in the MLOps space for ~10 years, and data is still the hardest unsolved open problem. Code is versioned using Git, data is stored somewhere else, and context often lives in a 3rd location like Slack or GDocs. This is why we built XetHub, a platform that enables teams to treat data like code, using Git. Unlike Git LFS, we don’t just store the files. We use content-defined chunking and Merkle Trees to dedupe against everything in history. This allows small changes in large files to be stored compactly. Read more here:…


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