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    MLOps

    Running Collaborative Experiments
    Sharing experiments with teammates can help you build models more efficiently.
    • Milecia McGregor
    • Dec 13, 2021 • 4 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, 2021 • 4 min read
    Adding Data to Build a More Generic Model
    You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.
    • Milecia McGregor
    • Oct 05, 2021 • 7 min read
    Using Experiments for Transfer Learning
    You can work with pretrained models and fine-tune them with DVC experiments.
    • Milecia McGregor
    • Aug 24, 2021 • 12 min read
    Tuning Hyperparameters with Reproducible Experiments
    Using DVC, you'll be able to track the changes that give you an ideal model.
    • Milecia McGregor
    • Jul 19, 2021 • 8 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, 2021 • 4 min read
    May ’21 Heartbeat
    Monthly updates are here! We've hit 30 team members! MLOps learning opportunities, tutorials with integrations, conference videos, Discord server growth, and more!
    • Jeny De Figueiredo
    • May 21, 2021 • 5 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, 2021 • 6 min read