Who can work with Qlik AutoML

User access to AutoML resources and functionality is determined by the following controls:

• User entitlement

• Assignment of specific security roles

• Permissions assigned via the User Default or custom roles

• Access to the space where the resources are located

This help topic outlines the access controls that apply for common actions in Qlik AutoML.

Access controls for ML experiments

Working with ML experiments generally involves two types of actions:

• Listing and opening ML experiments

• Creation, use, and management of ML experiments

Listing and opening ML experiments

Creation, use, and management of ML experiments

Experiment creation, use, and management involves the following actions:

• Creating ML experiments

• Deleting ML experiments

• Editing ML experiments

• Moving ML experiments between spaces

To perform these actions, you need:

• Professional or Full User entitlement

• Automl Experiment Contributor security role

• For experiments in shared spaces, one of the following space roles in the space where the ML experiment is located:

  • Owner (of the space)

  • Can manage

  • Can edit

In the case of moving between spaces, you need one of the above roles in both the current space and the destination space.

Access controls for ML deployments and predictions

Working with ML deployments and predictions generally involves the following action types:

• Listing and opening ML deployments

• Model deployment and creation of ML deployments

• Management of ML deployments

• Running predictions

• Model approval

Listing and opening ML deployments

To list and open ML deployments, you need:

• Professional or Full User entitlement

• Automl Experiment Contributor or Automl Deployment Contributor security role

• For ML deployments in shared spaces, one of the following space roles in the space where the ML experiment is located:

  • Owner (of the space)

  • Can manage

  • Can edit

• For ML deployments in managed spaces, one of the following space roles in the space where the ML experiment is located:

  • Owner (of the space)

  • Can manage

Model deployment and creation of ML deployments

Management of ML deployments

Managing ML deployments involves the following actions:

• Deleting ML deployments

• Duplicating ML deployments

• Editing ML deployment details

• Creating, editing, deleting, and changing owner of batch prediction configurations

• Creating, editing, and deleting prediction schedules

• Creating, renaming, and deleting model aliases in an ML deployment

• Moving ML deployments between spaces

To perform these actions, you need:

• Professional or Full User entitlement

• Automl Deployment Contributor security role

• Required space role in the space of the ML deployment (or, in the case of moving between spaces, in the current and destination space)

  • For deployments in shared spaces, one of the following:

    • Owner (of the space)

    • Can manage

    • Can edit

  • For deployments in managed spaces, one of the following:

    • Owner (of the space)

    • Can manage

Running predictions

Predictions can run as batch predictions (from a prediction configuration) or real-time predictions. You can use the Qlik AutoML connector to run predictions as well. The required permissions are the same regardless of how you run the predictions. For scheduled batch predictions, there are also requirements for the owner of the prediction configuration. See Prediction configuration ownership.

Users with Professional, Full User, and Analyzer entitlement can run predictions.

Professional or Full User entitlement

As a user with Professional or Full User entitlement, you need the following to run predictions:

• Automl Deployment Contributor security role

• Required space role in the space of the ML deployment

  • For deployments in shared spaces, one of the following:

    • Owner (of the space)

    • Can manage

    • Can edit

    • Can consume data

  • For deployments in managed spaces, one of the following:

    • Owner (of the space)

    • Can manage

    • Can consume data

Analyzer entitlement

As a user with Analyzer entitlement, you need the following to run predictions:

• Automl Deployment Contributor security role

• Required space role in the space of the ML deployment

  • For deployments in shared spaces, one of the following:

    • Owner (of the space)

    • Can consume data

  • For deployments in managed spaces, one of the following:

    • Owner (of the space)

    • Can consume data

Model approval

To activate and deactivate a model within an ML deployment, you need:

• Professional or Full User entitlement

• Automl Deployment Contributor security role

• Required space role in the space of the ML deployment

  • For deployments in shared spaces, one of the following:

    • Owner (of the space)

    • Can manage

    • Can edit

  • For deployments in managed spaces, one of the following:

    • Owner (of the space)

    • Can manage

• The Approve or reject your AutoML models permission set to Allowed in one of the following:

  • User Default settings (affects all users)

  • Custom role (only affects users with the custom role)

For more information about model approval, see Approving deployed models.

Administering experiments and ML deployments

Administering from Analytics or Insights activity centers

In the Analytics or Insights activity centers, tenant and analytics administrators can perform the following actions without any additional permissions:

• View all experiments and ML deployments in the space

• Delete experiments and ML deployments (only tenant administrator can delete these assets from another user's personal space)

For other actions, the administrator could require specific permissions, such as:

• Space roles

• Permissions assigned via User Default and custom roles

Administering from the Administration activity center

From the Administration activity center, tenant and analytics administrators, and users with specific permissions, can administer AutoML.

For more information and specific permissions for each administrator, see Administering Qlik AutoML and Working with model approval as an administrator.