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Model Registry for Machine Learning

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

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  • Collaboration and visibility

    Explore and search all models and associated information in a single place for your ML team

  • Deployment automation

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

  • Security and auditing

    Manage models with embedded access controls and see team members that worked with each model through its lifecycle

Use your Git repositories as the base for your model registry

See all model information, including code, data, and configurations, with your Git service as your single source of truth.

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

Answer questions like,

  • How was this model trained?
  • Who put this model into production?
  • What data were used to train this model?
  • and more.

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

Manage and tag your models as they're created and build a timeline of activities and information from experimentation to production.

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

Collaborate across your ML and data science team. Find, understand, and manage models, moving them between development and production.

Choose between GUI or CLI (or both!)

Register, track, and version models with command line or use Studio to visually search and compare models.

A terminal showing two examples of gto commands, "gto show" and "gto history churn"

Your model registry can be built completely within your terminal -- the optimal developer experience. Use Studio for a seamless point-and-click experience, suited for business-focused users.

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

Gain additional capabilities around data versioning, pipeline management, experiment tracking, training automation, and more with Iterative tools. Iterative's products are modular, so you can pick-and-choose which to use or continue using your existing tools.

Ready to build your model registry?

Reach out to one of our MLOps experts!

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