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Data Version Control in Real Life

We write about machine learning workflow. From data versioning and processing to model productionization. We share our news, findings, interesting reads, community takeaways.
Git Custom References for ML Experiments
In DVC 2.0, we’ve introduced a new feature set aimed at simplifying the versioning of lightweight ML experiments. In this post, we’ll dive into how exactly these new experiments work.
  • Peter Rowlands
  • Apr 19, 2021 • 8 min read
April '21 Heartbeat
Monthly updates are here! Loads from the Community, use cases, videos, 5 new hires, and more!
  • Jeny De Figueiredo
  • Apr 16, 2021 • 7 min read
March '21 Community Gems
A roundup of technical Q&A's from the DVC community. This month: remote storage integration, hyperparameter tuning, best practices for managing experiments and more.
  • Jeny De Figueiredo
  • Mar 31, 2021 • 5 min read
March '21 Heartbeat
Monthly updates are here! News, use cases, videos, journal publications and more!
  • Jeny De Figueiredo
  • Mar 15, 2021 • 6 min read
DVC 2.0 Release
Today is DVC 2.0 release day! Watch a video from DVC-team when we explain the new features and read more details in this blog post.
  • Dmitry Petrov
  • Mar 03, 2021 • 19 min read
February '21 Community Gems
A roundup of technical Q&A's from the DVC community. This month: best practices for config files, pipeline dependency management,and caching data for CI/CD. Plus a new CML feature to launch cloud compute with Terraform!
  • Elle O'Brien
  • Feb 26, 2021 • 8 min read
CML Pre-Release Notes: Automatically Train Models in the Cloud with CML 0.3.0
New features are here to make launching cloud compute for continuous integration workflows shorter, sweeter and easier than ever. Plus, a new GitHub Action to setup CML means more ways to use CML without our Docker container.
  • Elle O'Brien
  • Feb 22, 2021 • 4 min read
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