Direct lake Semantic model changed, but recognized as a new model in the deployment pipeline

Hi Anonymous It might be the case could you please check these and see if it resolves 

• Compare Metadata: Use Tabular Editor to check differences in annotations, IDs, and dataset names between test and production models.

• Fix Annotations: Update the test model's annotations to match the production model.

• Check Pipeline Settings: Ensure the pipeline links the test model to the correct production workspace.

• Revalidate: Validate the updated test model to ensure consistency.

Hi Anonymous,

Thank you for reaching out to the Microsoft fabric community forum. Thank you Akash_Varuna, for your inputs on this thread. After thoroughly reviewing the details you provided, I have identified few workarounds that may help resolve the issue. Please follow these steps:

The annotation "PBIService_Versioning_SaveVersion" in your production model indicates that the model was saved or modified in Power BI Service, which may have changed its internal metadata. This could be causing inconsistencies when comparing the test and production models during deployment.

For deployment pipelines to work smoothly, the metadata of the test and production models must match. If there are differences, Fabric might not recognize them as the same model. This can result in the test model being treated as a completely new model instead of an update, leading to deployment issues.

To fix this, try opening the production model in Power BI Desktop, saving it using the same version as the test model, and then re-uploading it to the production workspace. If the issue persists, use Tabular Editor to compare annotations between the models. Look for any discrepancies especially in entries like "PBIService_Versioning_SaveVersion" and remove or adjust them as needed before saving and redeploying.

Additionally, check the Model ID, Annotations, and any unique identifiers in Tabular Editor to ensure they match between the test and production models. If there are unintended differences, consider reverting the metadata to align with the production model. If the model was redeployed or duplicated in the test workspace, Power BI might have assigned it a new ID. Always verify that you're working with the same dataset rather than unintentionally creating a new one.

Yes, the metadata differences, particularly the __PBIService_Versioning_SaveVersion, are likely causing the deployment pipeline to recognize the modified model as a new one. Aligning the metadata using Tabular Editor and ensuring consistency in the Power BI Desktop version across test and production environments should resolve the issue.

Kindly refer to the below following links for more information:

Manage Direct Lake semantic models - Microsoft Fabric | Microsoft Learn
Develop Direct Lake semantic models - Microsoft Fabric | Microsoft Learn

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Thank you.