• Access the model registry
• Create machine learning models by using the UI
• Create a model version by using the UI
  • Delete a model
  • Delete a model version
  • Add artifacts to a model version
  • Delete artifacts from a model version
• Create machine learning models and model versions by using MLflow
  • Add artifacts, metrics, and parameters to a model version by using MLflow
• Link a model version to a CI/CD job
• Model versions and semantic versioning
  • Model version examples
• 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 supported features, see epic 9423.

Access the model registry

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

If Model registry is not available, ensure that it has been enabled.

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

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.

Create machine learning models by using the UI

To create a new machine learning model and model version by using the GitLab UI:

1. On the left sidebar, select Deploy > Model registry.
2. On the Model registry page, select Create Model.
3. Complete the fields:
   • Enter a unique name for your model name.
   • Optional. Provide a description for the model.
   • Optional. Create a version for the model along with a version description.
   • Upload any files, logs, metrics, or parameters associated with the model.
4. Select Create.

You can now view the newly created model in the model registry.

Create a model version by using the UI

To create a new model version:

1. On the model details page, select Create model version.
2. Complete the fields:
   • Enter a unique version number following semantic versioning.
   • Optional. Provide a description for the model version.
   • Upload any files, logs, metrics, or parameters associated with the model version.
3. Select on Create & import.

The new model version is now available in the model registry.

Delete a model

To delete a model and all its associated versions:

1. On the left sidebar, select Deploy > Model registry.
2. Find the model you want to delete.
3. Select the model name to view its details.
4. Select the vertical ellipsis () and Delete model.
5. Confirm the deletion.

Delete a model version

To delete a model version:

1. On the left sidebar, select Deploy > Model registry.
2. Find the model with a version you want to delete.
3. Select the model name to view its details.
4. Select the Versions tab.
5. Select the version name to view its details.
6. Select the vertical ellipsis () and Delete model version.
7. Confirm the deletion.

Add artifacts to a model version

To add artifacts to a model version:

1. On the left sidebar, select Deploy > Model registry.
2. Find the model.
3. Select the model name to view its details.
4. Select the Versions tab.
5. Select the version name to view its details.
6. Optional. Specify a path for the files to be uploaded to. For example config.
7. Use select to choose the files to upload.
8. Select Upload.

Alternatively, you can drag and drop files in the drop area. The artifacts are automatically uploaded.

Delete artifacts from a model version

To delete artifacts of a version:

1. On the left sidebar, select Deploy > Model registry.
2. Find the model.
3. Select the model name to view its details.
4. Select the Versions tab.
5. Select the version name to view its details.
6. Select the box next to each artifact you want to delete.
7. Select Delete.
8. Confirm the deletion.

Create machine learning models and model versions by using MLflow

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.

Add artifacts, metrics, and parameters to a model version by using MLflow

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 convenient 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:

• Major (integer): 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.

• Minor (integer): A change in the minor component signifies a non-breaking change, and that the consumer can safely use the new version without breaking, although the consumer might need to be updated to use its new functionality. For example, adding a non-mandatory feature column with a default value to the model is a minor bump, because when a value for the added column is not passed, inference still works.

• Patch (integer): 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.

• Prerelease (text): Represents a version that is not yet ready for production use. Used to identify alpha, beta, or release candidate versions of the model.

Model version examples

• Initial Release: 1.0.0 - The first release of the model, with no changes or patches.
• New Feature: 1.1.0 - A new non-breaking feature has been added to the model, incrementing the minor version.
• Bug Fix: 1.1.1 - A bug has been fixed in the model, incrementing the patch version.
• Breaking Change: 2.0.0 - A breaking change has been made to the model, incrementing the major version.
• Patch Release: 2.0.1 - A bug has been fixed in the model, incrementing the patch version.
• Prerelease: 2.0.1-alpha1 - A prerelease version of the model, with an alpha release.
• Prerelease: 2.0.1-rc2 - A release candidate version of the model.
• New Feature: 2.1.0 - A new feature has been added to the model, so the minor version is incremented.

Related topics

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