Scenario: working with ML models

This section describes the sequence of actions required to work with ML models.

The scenario for working with ML models consists of the following steps:

1. Adding an ML model

   You can add an ML model to Kaspersky MLAD in one of the following ways:

   • Upload an ML model created by Kaspersky specialists or by a certified integrator as part of the Kaspersky MLAD Model-building and Deployment Service. After an ML model is uploaded, it must be activated.
   • Manually create an ML model. Add neural network elements and/or elements based on diagnostic rules to the created ML model.
   • Create an ML model from a template. Create a template based on the relevant ML model in advance. If the original ML model used for the template was manually created, you can add neural network elements and/or elements based on diagnostic rules to the new ML model.
   • Clone a previously added ML model. When cloning an ML model that was created manually or from a template based on a manually created ML model, you can add neural network elements and/or the elements based on diagnostic rules to the cloned ML model.
2. Adding markups

   If you need to define specific time intervals for the data that an ML model can use for training or inference, create markups. To generate an inference indicator, specify the created markup in the settings of the corresponding ML model.

3. Training ML model elements

   The ML model needs to be trained before you can run inference on it. To do this, all neural network elements within the ML model need to be pretrained. ML model elements based on diagnostic rules are considered to be already trained.

   An ML model uploaded to Kaspersky MLAD has been previously trained by Kaspersky Lab experts or a certified integrator. ML models that are created from a template of an imported ML model or by cloning an imported ML model are also considered to be already trained. If necessary, you can change their training parameters and retrain the neural network elements.

   To generate a learning indicator, specify the created markup in the learning parameters of the neural network element.

4. Preparing an ML model for publication

   After its training is finished, prepare the ML model for publication. An ML model ready for publishing cannot be modified.

5. Publishing an ML model

   After preparing the ML model for publication, notify the officer responsible for publishing the ML model that the ML model is ready, or publish the ML model if you have the required permissions. If necessary, the system administrator can create a role that has the right to publish ML models and assign this role to the relevant employee.

6. Starting ML model inference

   Start inference of the ML model. During the inference process, the ML model analyzes telemetry data and registers incidents.

   ML model inference can be run on a published ML model as well as on a trained ML model.

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