Update how-to-use-batch-endpoint.md

Author: santiagxf

articles/machine-learning/how-to-use-batch-endpoint.md

@@ -253,7 +253,7 @@ A deployment is a set of resources required for hosting the model that does the
 
     1. Select __Register__.
 
-1. Now it's time to create an scoring script. Batch deployments require a scoring script that indicates how a given model should be executed and how input data must be processed. Batch Endpoints support scripts created in Python. In this case, we're deploying a model that reads image files representing digits and outputs the corresponding digit. The scoring script is as follows:
+1. Now it's time to create a scoring script. Batch deployments require a scoring script that indicates how a given model should be executed and how input data must be processed. Batch Endpoints support scripts created in Python. In this case, we're deploying a model that reads image files representing digits and outputs the corresponding digit. The scoring script is as follows:
 
     > [!NOTE]
     > For MLflow models, Azure Machine Learning automatically generates the scoring script, so you're not required to provide one. If your model is an MLflow model, you can skip this step. For more information about how batch endpoints work with MLflow models, see the dedicated tutorial [Using MLflow models in batch deployments](how-to-mlflow-batch.md).