---
sourceDocument: Asset Management
sourceDocumentLink: https://www.servicenow.com/docs/r/it-asset-management

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Asset Management

ft:clusterId :

    - itam

bundleId :

    - itam

workflow :

    - Technology


---

# Normalization of discovery models using machine learning

# Normalization of discovery models using machine learning {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 3 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Normalization of discovery models using machine learning

This feature in the Software Asset Management (SAM) application leverages machine learning (ML) to enhance the normalization of unrecognized discovered software in real time.
It focuses on improving the accuracy of normalized discovery models by predicting attributes such as version, full version, and edition.
Show full answer Show less  
ML normalization is available starting with the Brazil release and is accessible in regulated markets under ServiceNow Protected Platform (SPP) in Singapore and Australia. Customers can enable this capability by activating the **Software Asset Management -- Machine Learning Normalization** plugin and enabling the associated property for ML normalization.

## Key Features

* **Machine learning-based normalization:** Automatically predicts and normalizes software discovery model attributes including version, full version, and edition.
* **Plugin activation:** ML normalization requires activation of the `com.snsammlnormalization` plugin and enabling the property `com.snc.samp.enable.mlnormalization`.
* **Scheduled jobs:** Two scheduled jobs manage normalization:
  * **SAM-Normalize discovery models using content library rules:** Runs daily to normalize using content service rules.
  * **SAM-Normalize discovery models using machine learning:** Runs on-demand to further normalize models using ML predictions when the plugin is activated.
* **Normalization precedence:** Content service rules always override ML predictions if conflicts arise.
* **Machine learning status tracking:** ML normalization status and model version are tracked in the Software Discovery Model table, enabling transparency of normalization results.
* **Manual revert option:** Users can revert ML normalization values to reset discovery models to a "Match not Found" status, which removes both ML and content-based normalized values.
* **Normalization rules:** Defined rules determine normalization status based on which fields (publisher, product, version, edition, full version) are normalized for licensed and non-licensed products.

## Practical Benefits for ServiceNow Customers

* **Improved data accuracy:** Enhances the quality of software discovery data by automatically normalizing unrecognized software attributes with ML.
* **Real-time normalization:** ML normalization runs continuously alongside content rules, ensuring discovery data remains current and accurate.
* **Regulatory compliance:** Available in regulated markets, supporting compliance requirements for software asset management.
* **Flexibility and control:** Customers can opt in or out of ML normalization and manually revert normalization to maintain control over discovery data.
* **Transparency and auditability:** Normalization status and ML model versions are clearly tracked, aiding reporting and troubleshooting.  
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. Starting with the Brazil release, machine learning normalization capabilities are available to regulated markets for ServiceNow Protected Platform (SPP) in Singapore (SG) and Australia (AU).

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](https://www.servicenow.com/docs/6ydCjyeBRTYF3NVanLG~Xg "You can set default reconciliation properties such as grouping and reconciliation debugging."). 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
  {#ml-learning-sam__ul_vbj_tby_znb}

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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.  
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| 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 1. Normalization rules for licensed products]

{#ml-learning-sam__table_qyb_5pf_gnb}  
{#ml-learning-sam__table_zwk_3qf_gnb__entry__2}

| 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 |
[Table 2. Normalization rules for non licensed products]

{#ml-learning-sam__table_zwk_3qf_gnb}