Normalization of discovery models using machine learning

  • Release version: Yokohama
  • Updated January 30, 2025
  • 3 minutes to read
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    Summary of Normalization of discovery models using machine learning

    This feature in ServiceNow's Software Asset Management (SAM) application leverages machine learning (ML) to enhance the normalization of discovered software models in real time. Normalization improves the accuracy of software discovery by standardizing attributes such as version, full version, and edition. This process helps customers maintain cleaner and more reliable software inventory data.

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    Key Features

    • Machine Learning Normalization Activation: Enable ML normalization by activating the Software Asset Management – Machine Learning Normalization plugin (com.snsammlnormalization) and selecting the property Enable ML Normalization for discovered software (com.snc.samp.enable.mlnormalization).
    • Scheduled Jobs for Normalization:
      • SAM-Normalize discovery models using content library rules: Runs daily to normalize discovery models based on content rules, irrespective of ML plugin activation.
      • SAM-Normalize discovery models using machine learning: Runs on-demand and processes partially normalized models using ML predictions if the plugin is activated.
    • Normalization Status Tracking: ML normalization statuses are tracked in the Software Discovery Model [cmdbsamswdiscoverymodel] table with columns indicating ML prediction values, model version, and normalization status (such as ML normalized, reverted, or content overridden).
    • Content Rules Precedence: Weekly content rule updates may override ML predictions to ensure the latest content service rules take precedence for normalization accuracy.
    • Manual Reversion: Users can revert normalization values via the Software Discovery Model form, which removes both content and ML normalized values, reverting the model status to "Match not Found". Reverting ML normalized models does not disable content rules.
    • Normalization Rules: The system applies specific normalization rules for both licensed and non-licensed products to determine the normalization status based on the attributes normalized (e.g., publisher, product, version, edition, full version).

    Practical Benefits for ServiceNow Customers

    • Improves accuracy and completeness of software discovery data with real-time ML-enhanced normalization.
    • Reduces manual effort by automating the normalization process using both content rules and machine learning.
    • Ensures up-to-date normalization by combining ML predictions with regularly updated content library rules, maintaining data integrity.
    • Provides transparency and control through status tracking and the ability to revert normalization if needed.
    • Supports effective software asset management by standardizing discovery models, enabling better compliance and optimization insights.

    Use machine learning to improve your normalization rates in real time by normalizing your unrecognized discovered software.

    The Software Asset Management application uses machine learning to improve normalization of discovery models. The prediction values currently supported by machine learning are version, full version, and edition.

    Opt in for machine learning normalization by activating the Software Asset Management – Machine Learning Normalization (com.sn_sam_ml_normalization) plugin.

    Once the plugin is activated, ensure that the Enable ML Normalization for discovered software (com.snc.samp.enable.ml_normalization) property is selected. For more details on this property, see Software Asset Management properties. You can opt out of machine learning normalization by disabling this property. If you opt out, normalization of discovery models only takes place against the content service rules.

    The scheduled job, SAM-Normalize discovery models using content library rules, triggers on a daily basis and normalizes the discovery models based on the content rules. This scheduled job runs irrespective of whether the Software Asset Management – Machine Learning Normalization plugin is activated or not. If this plugin is activated, then the partially normalized discovery models are picked up by another scheduled job, SAM-Normalize discovery models using machine learning. The scheduled job, SAM-Normalize discovery models using content library rules is enhanced to invoke the on-demand scheduled job, SAM-Normalize discovery models using machine learning and also validates machine learning predictions.

    Once the scheduled job, SAM-Normalize discovery models using machine learning is complete, you can view the updated values in the following machine learning based columns in the Software Discovery Model [cmdb_sam_sw_discovery_model] table:
    • ML prediction values: Indicates the predicted values for the attributes.
    • ML model version: Indicates the model version that was used for predicting the attributes.
    • ML normalization status: Indicates the status of machine learning normalization. Values for this column include:
      • ML normalized: Discovery model is normalized by machine learning
      • Reverted: Discovery model is normalized by machine learning but the user reverted the normalized values
      • Content overridden: Machine learning predictions over-written by new content rules
    Note:
    The status of the scheduled job, SAM-Normalize discovery models using machine learning is tracked in the Software Asset Job Result [samp_job_log] table.
    As the content rules are always getting updated, the weekly scheduled job SAM-Normalize discovery models using content library rules picks up the discovery models normalized by machine learning and tries to normalize these models with the latest content rules. If the predicted values of machine learning differ from the predictive values of the content service, the machine learning predictions are overwritten with the content service values. The content service prediction values always get precedence over the machine learning prediction values.
    Note:
    For details on the normalization rules for the predictive values, refer to tables titled Normalization rules for licensed products andNormalization rules for non licensed products.
    You can manually normalize a discovery model by reverting the normalization values. When you revert normalizations in the Software Discovery Model form, all the normalized values, got from content and machine learning, are removed. The discovery model reverts to a status of Match not Found.
    Note:
    When you revert a discovery model normalized by machine learning, the content rules are not deactivated. However, if a discovery model is normalized only by content rules, then the content rules are deactivated.
    Table 1. Normalization rules for licensed products
    Fields Normalization status
    All fields are normalized
    Note:
    All the fields include publisher, product, version, edition, and full version.
    		 Normalized
    Only the publisher is normalized Publisher normalized
    If none of the fields are normalized: publisher, product, version, edition, full version Match not found
    Only product and publisher are normalized. Partially normalized
    Table 2. Normalization rules for non licensed products
    Fields Normalization status
    If only publisher and product are normalized Normalized
    Only the publisher is normalized Publisher normalized
    If none of the fields are normalized: publisher, product, version, edition, full version Match not found