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Deploying your model

The next step is to deploy your model. Deploying a model allows you to use it to generate predictions on new data.

The model refinement process is different for each project you work on. Once you have a model that meets the criteria for your use case, you can deploy it. This will create an ML deployment, which is available in Catalog.

For more information about deploying your models in Qlik AutoML, see Working with ML deployments.

Information noteAutoML is continually improving its model training processes. Your model metrics, as well as the algorithm for the model which you will deploy, might not be identical to those shown in the images on this page.
  1. In the model metrics table, click the checkbox for the model version you want to deploy. Based on model performance and absence of data leakage issues, this will likely be the top-performing model from v3.

  2. Click Deploy in the bottom right corner of the page.

  3. Type a name for your deployment, such as Customer churn deployment.

    Alternatively, keep the default deployment name.

  4. If needed, adjust the space, description, and tags.

  5. Click Deploy.

    Deploying a model in Qlik AutoML

    Selecting the 'Deploy' option for the chosen model.

Your new ML deployment will now be available in Catalog.

Click Open, or navigate back to Catalog and open the ML deployment. The ML Model Management interface will open.

ML Model Management interface for an ML deployment

Deployment overview for the new model in the ML model management interface.

You can now proceed to creating predictions with your ML deployment.

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