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Open Source MLOps Tool DVC Adds First-of-its-Kind Experiment Versioning

Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, announced the latest release of Data Versioning Control (DVC), introducing industry-first, experiment versioning. Experiment versioning gives developers an easy way to save, compare, and reproduce ML experiments at scale in ways that neither traditional software version control nor existing experiment tracking tools can.

“Experiment tracking tools have come a long way. Users no longer need to log experiment information in spreadsheets or notebooks,” said Dave Berenbaum, technical product manager at Iterative. “But current experiment tracking tools usually provide an API to log experiment information, a database to store it, and a dashboard to compare and visualize. DVC experiment versioning builds on modern version control principles and technology to address experiment tracking needs and give developers the most integrated way to iterate their experiments.”

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Experiment versioning in DVC builds on modern version control principles and technology to address experiment tracking needs and give developers the easiest and most complete way to iterate their experiments. Experiment versioning is lightweight, using an existing tech stack eliminating the need for additional services. Automated reproduction saves time and complexity while providing confidence and audit-ability, while distributed and flexible collaboration enables any size team to generate experiments individually and share them as they choose.

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With experiment versioning, data science teams can:

  • Restore or reproduce any experiment automatically
  • Log experiments end-to-end and track changes introduced by each
  • Keep experiments connected to their Git repo, with no external services needed

With open tools and formats, Iterative is cloud-agnostic, providing greater flexibility and removing the need and lock-in for proprietary AI Platforms.

DVC provides users with a Git-like interface for versioning data, models, and pipelines, bringing version control to machine learning and solving the challenges of reproducibility. Experiment versioning extends DVC’s capabilities beyond simple experiment tracking.

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