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 your license.

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.

Requirements and permissions

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

Deploying models

Do the following:

1. Open an experiment from Catalog.

2. In the model view, select the model you want to deploy.

3. Click Deploy in the bottom right corner.

4. Enter a name and space, and, optionally, a description and 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.

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

Editing name and other details of ML deployments

Do the following:

1. Open an ML deployment from 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 Catalog.

Managing ML deployment jobs

Tenant admins can stop or cancel deployment jobs from the Management Console. For more information, see Managing experiments and ML deployments.

Configuring notifications

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