Release
The DVC 3.0 Stack: Beyond the Command Line
DVC 3.0 introduces a stack of tools outside the command line to bring it closer to
where you work (in code, IDE, web) while also focusing on DVC fundamentals.
Organize Your Storage with DVC Cloud Versioning
DVC cloud versioning makes it easy to take full advantage of your cloud
provider’s built-in versioning capabilities.
Real-time visualization of Computer Vision model training with DVC and Iterative Studio
Save time and resources by tracking your deep learning experiments in real-time with DVC and Iterative Studio.
Instant Experiment Tracking: Just Add DVC!
Experiment tracking in DVC with a few lines of Python.
Building a GitOps ML Model Registry with DVC and GTO
Got your data and model versioning down? ✅ Learn how to take your projects to the next level by creating a model registry right in your project's Git repo
Deploy ML models to k8s and SageMaker with a single line of code
MLEM takes the evil configs out of Kubernetes and SageMaker to make your life easier just in time for Halloween 🎃
DVC and Hydra integration
Use Hydra and DVC in the same project and benefit from the best of both tools.
CML Cloud Runners for Model Training in Bitbucket Pipelines
Use CML from a Bitbucket pipeline to provision an AWS EC2 instance and (re)train a machine learning model.
Git-backed Machine Learning Model Registry to bring order to chaos
🚀 As Machine Learning projects and teams grow, keeping track of all the models and their production status gets increasingly complex. Iterative Studio's Git-backed Model Registry solves this.
