Deploying models

You can deploy models from experiments in personal or shared spaces. ML deployments can be published to managed spaces. Each ML deployment is generated from a single algorithm from a single experiment version. The number of ML deployments is limited by the subscription.

The tier included in a Qlik Cloud subscription defines a maximum number of deployed models that can be created across all tenants created within the license. This consumption limit is defined per model, meaning that multiple ML deployments created from a single model count as a single deployed model. If you have reached the maximum number of deployed models, delete one or more existing deployed models or upgrade the subscription to a higher tier.

When you have deployed a model, the model is marked with a icon in the ML experiment. This icon can be clicked to go directly to the ML deployment. The ML deployment also becomes available in the catalog.

Requirements and permissions

To learn more about the user requirements for working with ML deployments and predictions, see Working with ML deployments.

Deploying models

You can deploy a model from the Data, Models, or Analyze tab.

From the Data or Analyze tabs

Do the following:

1. In the toolbar, use the drop down menu to select a model.

2. Click .

3. Click Deploy.

4. Enter a name and space, and, optionally, edit the description and add tags.

5. Optionally, select Enable real-time API access. This option is controlled by your license and enables predictions where the apply data is in the API request and the results are in the response.

6. Click Deploy.

From the Models tab

Do the following:

1. In the Model metrics table, click next to a model.

2. Click Deploy.

3. Enter a name and space, and, optionally, edit the description and add tags.

4. Optionally, select Enable real-time API access. This option is controlled by your license and enables predictions where the apply data is in the API request and the results are in the response.

5. Click Deploy.

You should now be able to find your ML deployment in the catalog.

Editing name and other details of ML deployments

Do the following:

1. Open an ML deployment from the catalog.

2. Click by the ML deployment name.

3. Edit the name or other details.

Deleting ML deployments

You can delete an ML deployment from the catalog.

Managing ML deployment jobs

Tenant admins can stop or cancel deployment jobs from the Administration activity center. For more information, see Administering Qlik AutoML.

Configuring notifications

You can receive notifications when a model is deployed from an experiment. For more information, see Configuring notifications for Qlik AutoML.