Update how-to-use-batch-azure-data-factory.md

Author: santiagxf

articles/machine-learning/how-to-use-batch-azure-data-factory.md

@@ -89,7 +89,7 @@ The pipeline requires the following parameters to be configured:
 | --------------------- | -------------|------------- |
 | `endpoint_uri`        | The endpoint scoring URI  | `https://<endpoint_name>.<region>.inference.ml.azure.com/jobs` |
 | `poll_interval`       | The number of seconds to wait before checking the job status for completion. Defaults to `120`.  | `120` |
-| `endpoint_input_uri`  | The endpoint's input data. Multiple data input types are supported. Ensure that the manage identity you are using for executing the job has access to the underlying location. Alternative, if using Data Stores, ensure the credentials are indicated there.  | `azureml://datastores/.../paths/.../data/` |
+| `endpoint_input_uri`  | The endpoint's input data. Multiple data input types are supported. Ensure that the managed identity you are using for executing the job has access to the underlying location. Alternative, if using Data Stores, ensure the credentials are indicated there.  | `azureml://datastores/.../paths/.../data/` |
 | `endpoint_input_type`  | The type of the input data you are providing. Currently batch endpoints support folders (`UriFolder`) and File (`UriFile`). Defaults to `UriFolder`.  | `UriFolder` |
 | `endpoint_output_uri` | The endpoint's output data file. It must be a path to an output file in a Data Store attached to the Machine Learning workspace. Not other type of URIs is supported. You can use the default Azure Machine Learning data store, named `workspaceblobstore`. | `azureml://datastores/workspaceblobstore/paths/batch/predictions.csv` |
 
@@ -114,7 +114,7 @@ The pipeline requires the following parameters to be configured:
 | `client_secret`       | The client secret of the service principal used to invoke the endpoint  | `ABCDEFGhijkLMNOPQRstUVwz` |
 | `endpoint_uri`        | The endpoint scoring URI  | `https://<endpoint_name>.<region>.inference.ml.azure.com/jobs` |
 | `poll_interval`       | The number of seconds to wait before checking the job status for completion. Defaults to `120`.  | `120` |
-| `endpoint_input_uri`  | The endpoint's input data. Multiple data input types are supported. Ensure that the manage identity you are using for executing the job has access to the underlying location. Alternative, if using Data Stores, ensure the credentials are indicated there.  | `azureml://datastores/.../paths/.../data/` |
+| `endpoint_input_uri`  | The endpoint's input data. Multiple data input types are supported. Ensure that the managed identity you are using for executing the job has access to the underlying location. Alternative, if using Data Stores, ensure the credentials are indicated there.  | `azureml://datastores/.../paths/.../data/` |
 | `endpoint_input_type`  | The type of the input data you are providing. Currently batch endpoints support folders (`UriFolder`) and File (`UriFile`). Defaults to `UriFolder`.  | `UriFolder` |
 | `endpoint_output_uri` | The endpoint's output data file. It must be a path to an output file in a Data Store attached to the Machine Learning workspace. Not other type of URIs is supported. You can use the default Azure Machine Learning data store, named `workspaceblobstore`. | `azureml://datastores/workspaceblobstore/paths/batch/predictions.csv` |