Cloning an ML model

System administrators and users who have the Copy models permission from the Manage ML models group of rights can clone ML models.

You can create an ML model by cloning a previously added ML model. When cloning, a new ML model is created. The new ML model contains the same elements, parameters of the ML model and its elements, as well as the training state of the neural network elements as the ones of the ML model being cloned at the time of its cloning.

When cloning an ML model that was created manually or from a template based on a manually created ML model, you can add neural network elements and/or the elements based on diagnostic rules to the cloned ML model, as well as modify or delete them.

When cloning an ML model that was imported into the application or created using a template based on an imported ML model, you cannot change the set of elements of the cloned ML model.

Before running inference, you can change the training settings and retrain the neural network elements of the copied ML model. You can also start inference after the ML model has been published.

To clone an ML model:

1. In the main menu, select the Models section.
2. In the asset tree, select the ML model that you want to copy.

   A list of options appears on the right.

3. In the upper-right corner of the window, click the Copy model () icon.

   The Model copying pane appears on the right.

4. In the Name field, specify the ML model name.

   The ML model name must not be longer than 100 characters.

   By default, an ML model is assigned a name in the following format: < name of the original ML model>_Cloned_ <date and time of cloning>.

5. In the Asset drop-down list, select the asset to which you want to assign the new ML model.
6. Click the Save button.

The new ML model displays in the Models group of the asset tree. The Models group is created automatically and displayed as part of the selected section of the asset tree. The Models group contains the Neural networks and Rules subgroups for storing ML model elements based on neural networks and diagnostic rules.