• Access the model registry
• Exploring models, model versions and model candidates
• Creating machine learning models and model versions
• Upload files, log metrics, log parameters to a model version
• Link a model version to a CI/CD job
• Model versions and semantic versioning
• Related topics

Model registry

Model registry allows data scientists and developers to manage their machine learning models, along with all metadata associated with their creation: parameters, performance metrics, artifacts, logs and more. For the full list of currently supported features, see epic 9423.

Access the model registry

To set the model registry visibility level to public, private or disabled:

1. On the left sidebar, select Search or go to and find your group.
2. Select Settings > General.
3. Expand Visibility, project features, permissions.
4. Under Model registry, ensure the toggle is on and select who you want to have access. Users must have at least the Reporter role to modify or delete models and model versions.

Exploring models, model versions and model candidates

To access the model registry, from the left sidebar, select Deploy > Model registry.

Creating machine learning models and model versions

Models and model versions can be created using the MLflow client compatibility. For more information about how to create and manage models and model versions, see MLflow client compatibility. You can also create models directly on GitLab by selecting Create Model on the Model registry page.

Upload files, log metrics, log parameters to a model version

Files can either be uploaded to a model version using:

• The package registry, where a model version is associated to a package of name <model_name>/<model_version>.
• The MLflow client compatibility. View details.

Users can log metrics and a parameters of a model version through the MLflow client compatibility, see details

Link a model version to a CI/CD job

When creating a model version through a GitLab CI/CD job, you can link the model version to the job, giving easy access to the job’s logs, merge request, and pipeline. This can be done through the MLflow client compatibility. View details.

Model versions and semantic versioning

The version of a model version in GitLab must follow Semantic Version specification. Using semantic versioning facilitates model deployment, by communicating which if a new version can be deployed without changes to the application:

• A change in the major component signifies a breaking change in the model, and that the application that consumes the model must be updated to properly use this new version. A new algorithm or the addition of a mandatory feature column are examples of breaking changes that would require a bump at the major component.

• A change in the minor component signifies a non-breaking change, and that the consumer can safely use the new version without breaking, although it might need to be updated to use its new functionality. For example, adding a non-mandatory feature column to the model is a minor bump, because when that feature is not passed, it will still work.

• A change in the patch component means that a new version is out that does not require any action by the application. For example, a daily retrain of the model does not change the feature set or how the application consumes the model version. Auto updating to a new patch is a safe update.

Related topics

• Development details, feedback, and feature requests in epic 9423.