Merge pull request #289024 from ssindhub/sindhu-ADF-ML-activity-pipeline-name-delay

Update transform-data-machine-learning-service.md

Author: v-dirichards

articles/data-factory/transform-data-machine-learning-service.md

@@ -63,11 +63,11 @@ mlPipelineId | ID of the published Azure Machine Learning pipeline | String (or
 experimentName | Run history experiment name of the Machine Learning pipeline run | String (or expression with resultType of string) | No
 mlPipelineParameters | Key, Value pairs to be passed to the published Azure Machine Learning pipeline endpoint. Keys must match the names of pipeline parameters defined in the published Machine Learning pipeline | Object with key value pairs (or Expression with resultType object) | No
 mlParentRunId | The parent Azure Machine Learning pipeline run ID | String (or expression with resultType of string) | No
-dataPathAssignments | Dictionary used for changing datapaths in Azure Machine learning. Enables the switching of datapaths | Object with key value pairs | No
+dataPathAssignments | Dictionary used for changing datapaths in Azure Machine Learning. Enables the switching of datapaths | Object with key value pairs | No
 continueOnStepFailure | Whether to continue execution of other steps in the Machine Learning pipeline run if a step fails | boolean | No
 
 > [!NOTE]
-> To populate the dropdown items in Machine Learning pipeline name and ID, the user needs to have permission to list ML pipelines. The UI calls AzureMLService APIs directly using the logged in user's credentials.  
+> To populate the dropdown items in Machine Learning pipeline name and ID, the user needs to have permission to list ML pipelines. The UI calls AzureMLService APIs directly using the logged in user's credentials.  The discovery time for the dropdown items would be much longer when using Private Endpoints.
 
 ## Related content
 See the following articles that explain how to transform data in other ways: