Managing ML models

This section provides instructions on working with ML models, ML model templates and markups.

The functionality is available after a license key is added.

ML models, templates of ML models and markups are functional elements of the monitored asset hierarchical structure. The hierarchical structure is displayed as an asset tree.

In Kaspersky MLAD, ML models can be imported, created manually, copied, or created based on a template. If you created the ML model manually, cloned a manually created model, or created the model from a template based on a manually created model, you can add predictive elements, elliptic envelope-based elements, and/or diagnostic rule-based elements to the new model.

After training the ML model elements and checking the results of their training, you can run historical or streaming inference on the ML model. As a result of inference, ML model elements register incidents and also generate artifacts that can be viewed under Monitoring and History.

You can publish the ML model if needed. You can run historical or streaming inference on a published ML model.

In the Models section, you can create markups for generating learning indicators or inference indicators. If necessary, you can edit, clone, or delete markups.

In this section About ML models About statuses and states of ML models and their elements About ML model templates About markups About conditions included in markups and diagnostic rules Scenario: working with ML models Search and filter objects in the Models section Working with markups Working with imported ML models Working with manually created ML models Cloning of the ML model element Removing an ML model element Cloning an ML model Working with ML model templates Changing the parameters of an ML model Training an ML model predictive element Training an elliptic envelope-based ML model element Viewing the training results of an ML model element Starting and stopping ML model inference Viewing the data flow graph of an ML model Preparing an ML model for publication Publishing an ML model Removing an ML model

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