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.
Your experience is different if your tenant administrator has turned on the New platform navigation setting in the Management Console. For example, ML Model Management is renamed to ML deployment and there are updates to the navigation bar. For more information about other aspects of the new navigation experience, see New platform navigation.
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 Catalo.
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:
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In the toolbar, use the drop down menu to select a model.
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Click
.
-
Click
Deploy.
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Enter a name and space, and, optionally, edit the description and add tags.
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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.
-
Click Deploy.
From the Models tab
Do the following:
-
In the Model metrics table, click
next to a model.
-
Click
Deploy.
-
Enter a name and space, and, optionally, edit the description and add tags.
-
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.
-
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:
- Open an ML deployment from Catalog.
-
Click
by the ML deployment name.
-
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 (or Administration, if you are using the new platform navigation). 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.