What causes Fabric Pipeline Error 4126?

The Azure ML pipeline itself has a logic, compute, or data error causing run failure. Compute cluster or instance used by the ML pipeline ran out of resources or timed out. Input data passed to the ML pipeline is malformed, missing, or incompatible. A step within the Azure ML pipeline (e.g., training, scoring) threw an unhandled exception

How do I fix Fabric Pipeline Error 4126?

Step 1: Note the Azure ML pipeline run ID ('%amlPipelineRunId;') from the error message — you will need this to locate the specific run in Azure Machine Learning Studio.. Step 2: Navigate to your Azure Machine Learning workspace in the Azure portal or ML Studio and locate the failed run using the run ID.. Step 3: Review the run's detailed logs, step outputs, and error messages within Azure ML Studio to identify the root cause of the failure.. Step 4: Fix the underlying issue in the Azure ML pipeline (e.g., correct data inputs, fix pipeline step code, increase compute quota, or resolve dependency issues).. Step 5: Once the Azure ML pipeline is fixed and validated, re-trigger the Fabric pipeline to confirm the issue is resolved.

High severityconfigurationMicrosoft Fabric →

Power BI Refresh Error:
Fabric Pipeline Error 4126

What does this error mean?

The Azure Machine Learning pipeline run invoked from a Fabric pipeline has failed. The failure originates within the ML pipeline itself and requires investigation in the Azure Machine Learning workspace.

Common causes

1The Azure ML pipeline itself has a logic, compute, or data error causing run failure
2Compute cluster or instance used by the ML pipeline ran out of resources or timed out
3Input data passed to the ML pipeline is malformed, missing, or incompatible
4A step within the Azure ML pipeline (e.g., training, scoring) threw an unhandled exception

How to fix it

1Step 1: Note the Azure ML pipeline run ID ('%amlPipelineRunId;') from the error message — you will need this to locate the specific run in Azure Machine Learning Studio.
2Step 2: Navigate to your Azure Machine Learning workspace in the Azure portal or ML Studio and locate the failed run using the run ID.
3Step 3: Review the run's detailed logs, step outputs, and error messages within Azure ML Studio to identify the root cause of the failure.
4Step 4: Fix the underlying issue in the Azure ML pipeline (e.g., correct data inputs, fix pipeline step code, increase compute quota, or resolve dependency issues).
5Step 5: Once the Azure ML pipeline is fixed and validated, re-trigger the Fabric pipeline to confirm the issue is resolved.

Frequently asked questions

Where exactly do I find the Azure ML error logs referenced in this error?

Go to your Azure Machine Learning workspace, navigate to 'Jobs' or 'Experiments', and search for the run using the '%amlPipelineRunId;' value from the error message. Each pipeline step will have its own logs accessible from the run detail view.

Does fixing the Azure ML pipeline automatically resolve the Fabric pipeline failure?

No. After fixing and validating the Azure ML pipeline, you must manually re-trigger or schedule the Fabric pipeline to confirm end-to-end success. The Fabric pipeline will not automatically retry after an Error 4126.