Managing ML models

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

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. After adding and training an ML model in Kaspersky MLAD, you can publish it. You can also run a historical or stream inference for the trained or published ML model, and view the data flow graph in the ML model.

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

In this section Scenario: working with ML models Working with markups Working with imported ML models Working with manually created ML models Cloning an ML model Working with ML model templates Changing the parameters of an ML model Training a neural network element of an ML model Viewing the training results of an ML model element Preparing an ML model for publication Publishing an ML model Starting and stopping ML model inference Viewing the data flow graph of an ML model Removing an ML model

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