About statuses and states of ML models and their elements

The statuses and states of ML models and their elements signify sequences of steps completed by the user under Models.

ML model elements can take the following statuses:

Not trained. This status is assigned to an ML model element if training has not started or completed with an error. This status is also displayed for elements that were trained previously, but whose settings have been changed. The asset tree displays the icon to the left of the untrained element names.
Trained. This status is assigned to an ML model element if training has been successfully completed or if the element is not subject to training. The asset tree displays the icon to the left of the trained element names.

An ML model can be assigned one of the following statuses:

Not activated. The ML model is imported but is not activated. The asset tree displays the icon to the left of the ML model name.
Not trained. The ML model activated or created manually. The ML model contains untrained elements. The asset tree displays the icon to the left of the ML model name.
Trained. All the elements in the ML model have been trained or no training is required. Inference can be run on a trained ML model. The asset tree displays the icon to the left of the ML model name.
Ready for publication. The ML model is ready for publishing and cannot be modified. The asset tree displays the icon to the left of the ML model name.
Published. The ML model is published. Inference can be run on a published ML model. The asset tree displays the icon to the left of the ML model name.

The states of ML models and their elements are displayed to the right of the ML model name when viewing a specific ML model, and in the asset tree. The table below lists the states of ML models and their elements in Kaspersky MLAD.

Statuses of ML models and elements of ML models

Name of the state when viewing the ML model	Symbol of the state in the asset tree	ML model	ML model element	Description
Not used				This state is assigned to an ML model unless inferenced or trained earlier, or if the model was viewed after inference or training finished. The asset tree does not display this status.
Training completed with error	TRN ERR			This state is assigned to ML model elements whose training finished with an error. An ML model is also assigned this state if containing at least one element whose training finished with an error. After viewing the training results, the ML model is assigned a state of Not used.
Training in progress	TRN			This status is assigned to an ML model element that is currently undergoing training. The model itself is assigned this status if it contains elements that are undergoing training and unless it contains any elements whose training completed with an error.
Queued for training	TRN Q			An ML model element is assigned this state if the model has started learning, but the training is running for a different element. An ML model is assigned this state if all of its elements are queued for training.
Training successfully completed	TRN DONE			This state is assigned to a successfully trained ML model element. An ML model is assigned this state if all of its elements have been successfully trained. After viewing the training results, the ML model is assigned a state of Not used.
Historical inference in progress	INFR HIST			This state is assigned to an ML model running historical inference. You can view the inference results under History. Incidents logged during inference are displayed under Incidents.
Queued for inference	INFR Q			This status is assigned to an ML model currently running historical inference, while a different model is currently running historical inference.
Historical inference completed	INFR DONE			This state is assigned to an ML model that has finished historical inference. You can view the inference results under History. Incidents logged during inference are displayed under Incidents. An ML model is assigned a state of Not used after being viewed.
Streaming inference in progress	INFR STRM			This state is assigned to the ML model running streaming inference. You can view the inference results under Monitoring. Incidents logged during inference are displayed under Incidents.

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