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    Release

    Turn Visual Studio Code into a machine learning experimentation platform with the DVC extension
    Today we are releasing the DVC extension, which brings a full ML experimentation platform to Visual Studio Code.
    • Rob de Wit
    • Jun 14, 20223 min read
    Productionize your models with MLEM in a Git-native way
    Introducing MLEM - one tool to run your models anywhere.
    • Alexander Guschin
    • Jun 01, 20225 min read
    Machine Learning Workloads with Terraform Provider Iterative
    Today we introduce painless resource orchestration for your machine learning projects in conjunction with HashiCorp Terraform.
    • Maria Khalusova
    • Apr 27, 20223 min read
    Don't Just Track Your ML Experiments, Version Them
    ML experiment versioning brings together the benefits of traditional code versioning and modern day experiment tracking, super charging your ability to reproduce and iterate on your work.
    • Dave Berenbaum
    • Dec 07, 20214 min read
    Easy Stuctural Refactors to Python Source Code
    Simple, hassle-free, dependency-free, AST based source code refactoring toolkit.
    • Batuhan Taskaya
    • Sep 24, 20212 min read
    Introducing DVC Studio
    🚀 We are excited to release DVC Studio, the online UI for DVC and CML. Use DVC Studio for ML versioning, visualization, teamwork and no-code automation on top of DVC and Git. Read all about the exciting features and watch videos to get started quickly.
    • Tapa Dipti Sitaula
    • Jun 02, 20214 min read
    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, 20216 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, 202114 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, 20213 min read