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Git-backed Model Registry for Machine Learning

Use your Git repositories to build a model registry with model versioning, lineage, and lifecycle management.

  • Organization, Discovery and Collaboration

    Curate your models in a central dashboard that facilitates model discovery across all your ML projects

  • Deployment automation

    Track, deploy, and manage models through the development lifecycle and integrate with CI/CD solutions like Heroku

  • Unifying ML projects with DevOps

    Manage model lifecycle through Git commits and tags, unifying ML projects with the best DevOps practices every step of the way

Build your model registry on top of Git

Reuse existing Git infrastructure for managing ML models together with code, data and metrics. With git as your single source of truth, use GitOps for model deployment.

An example showing git tags and it's respective stage in Iterative Studio dashboard.

Answer questions like,

  • Which model version is currently in production?
  • Who created this model version and when?
  • Which code and dataset was used to train it?
  • Who put this model into production?
  • and more.

Manage your ML models through the entire lifecycle, from development to production.

Create versions and assign stages to models to build a timeline of model actions from experimentation to production.

An example in Iterative's Studio product. Shows a page where you can view list of all your model.

Manage the lifecycle of each model as it moves through staging, production and other stages. See at a glance which model versions are in which stage.

Use the interface of your choice

Register, track, and version models with command line or from Iterative Studio.

An example using both cli terminal and Iterative Studio dashboard to get info on project.

Use the interface that you’re most comfortable with, whether you’re a data scientist who likes APIs, or a manager who prefers a web UI, or a DevOps engineer who works best with the command line interface (CLI) and uses GitOps principles for model deployment.

Build your model registry with Iterative's ecosystem of tools

Use Iterative tools for even more benefits, like linking versioned data with your code and models.

An example of the iterative.ai ecosystem. The example shows two layers, the bottom layer representing third part tools and the top layer represents iterative.ai products.

Gain additional capabilities around data versioning, pipeline management, experiment tracking, training automation, and more with Iterative tools including DVC and CML. Iterative's products are modular, so you can pick those components that extend what you already have. Because you work with your existing cloud and DevOps infrastructure, you can avoid vendor lock-in.

Ready to build your model registry?

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