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.

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. 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.

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

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