Working with ML deployments

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

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.