Skip to main content Skip to complementary content

Working with ML predictions

Once you have deployed your machine learning model, you can use the model to create predictions. These predictions can be used to make more efficient and informed decisions based on your data.

You can create and edit ML deployments and generate predictions in personal or shared spaces. You can also publish ML deployments to managed spaces and generate predictions. Access to ML deployments is controlled through the space. For more information about spaces, see Working in spaces.

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.

Learn more

Did this page help you?

If you find any issues with this page or its content – a typo, a missing step, or a technical error – let us know how we can improve!