Kaspersky Machine Learning for Anomaly Detection

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

A section of a hierarchical structure representing, for example, a plant, a shop, or a separate unit of a monitored asset.

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

A set of criteria used to determine the data time intervals on which the ML model performs the training.

inference indicators

A set of criteria used to determine the data time intervals on which the ML model performs the inference.