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Working with ML deployments

After training and refining a model, you can deploy the model to make predictions.

ML deployments can be created in personal, shared, and managed spaces.

Requirements and permissions

To work with ML deployments and the prediction configurations within them, you must have the following:

  • Professional or Full User entitlement

  • View and create ML deployments: Automl Deployment Contributor or Automl Experiment Contributor security role

  • Edit and delete ML deployments: Automl Deployment Contributor security role

  • Configure and run predictions from the ML deployment: Automl Deployment Contributor security role

  • Required permissions in the space where the ML deployments are located.

Predictions are created as datasets. Therefore, the same requirements for working with data sources in Qlik Cloud apply to working with prediction output (such as using it in a Qlik Sense app). You must have the Private Analytics Content Creator role to create datasets in your personal space.

For scheduled predictions, there are also requirements for the owner of the prediction configuration.

For more information, see:

Workflow

The following steps are an example of how to work with ML deployments and predictions.

  1. Deploy your model

    Deploy the model you want to use to make predictions.

    Deploying models

  2. Make predictions

    Make manual or scheduled predictions on datasets or use the prediction API.

    Creating predictions on datasets

  3. Visualize the predictive insights

    Load the generated prediction data into an app and create visualizations.

    Visualizing SHAP values in Qlik Sense apps

  4. Explore the data with what-if scenarios

    Integrate the prediction API into an app to get real-time predictions. This allows you to try out what-if scenarios by changing feature values and getting predicted outcomes for the new values. The record is passed to the ML deployment via API and a response is received in real time. For example, what would happen to the risk of customer churn if we changed the plan type or increased the base fee?

  5. Take action

    Analyze the predictive insights and scenarios to find out which actions to take. Qlik Application Automation helps you automate the actions and provides specific templates for machine learning use cases. For more information about automations, see Qlik Application Automation.

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