Update how-to-batch-scoring-script.md

Author: santiagxf

articles/machine-learning/how-to-batch-scoring-script.md

@@ -51,6 +51,10 @@ deployment = BatchDeployment(
 On [Azure Machine Learning studio portal](https://ml.azure.com), when creating a new deployment, you will be prompted for an scoring script and dependencies as follows:
 
 :::image type="content" source="./media/how-to-batch-scoring-script/configure-scoring-script.png" alt-text="Screenshot of the step where you can configure the scroing script in a new deployment.":::
+
+MLflow models don't require an scoring script as Azure Machine Learning can automatically generate it for you. However, if you want to customize how inference is executed you can still indicate it:
+
+:::image type="content" source="./media/how-to-batch-scoring-script/configure-scoring-script-mlflow.png" alt-text="Screenshot of the step where you can configure the scroing script in a new deployment when the model has MLflow format.":::
 
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@@ -103,9 +107,9 @@ The `run()` method should return a Pandas `DataFrame` or an array/list. Each ret
 > [!IMPORTANT]
 > __How to write predictions?__
 > 
-> Use __arrays__ when you need to output a single prediction. Use __pandas DataFrames__ when you need to return multiple pieces of information. For instance, for tabular data, you may want to append your predictions to the original record. Use a pandas DataFrame for this case. For file datasets, __we still recommend to output a pandas DataFrame__ as they provide a more robust approach to read the results.
-> 
-> Although pandas DataFrame may contain column names, they are not included in the output file. If needed, please see [Customize outputs in batch deployments](how-to-deploy-model-custom-output.md).
+> Whatever you return in the `run()` function will be appended in the output pedictions file generated by the batch job. It is important to return the right data type from this function. Return __arrays__ when you need to output a single prediction. Return __pandas DataFrames__ when you need to return multiple pieces of information. For instance, for tabular data you may want to append your predictions to the original record. Use a pandas DataFrame for this case. Although pandas DataFrame may contain column names, they are not included in the output file.
+>
+> If you need to write predictions in a different way, you can [customize outputs in batch deployments](how-to-deploy-model-custom-output.md).
 
 > [!WARNING]
 > Do not not output complex data types (or lists of complex data types) in the `run` function. Those outputs will be transformed to string and they will be hard to read.