The Verta Model Registry is a place to find, publish, and use ML models (or model pipeline components.) Similar to container registries like DockerHub or Python package registries like PyPi, builders of models and ML pipelines can publish ready-to-consume components to the Verta Model Registry.

Where does a Model Registry fit into the ML Lifecycle?

Model Registry is like a staging area between experimentation and production where model developers go from hundreds of experiments (in an experiment management system) to just a handful that make it to production.

What are the benefits of a model registry?

  • With model documentation and versioning, it enables easy handoff to consumers of your models.
  • It provides a central place to impose governance and controls on your models.
  • It allows you to automate your release processes by integrating with tools CI/CD tools like Jenkins.
Last modified 4mo ago