Merge pull request #224600 from santiagxf/santiagxf/azureml-mlflow-folder

Adding instructions to get the model's folder name for MLflow

Author: Stacyrch140

articles/machine-learning/how-to-deploy-mlflow-models-online-endpoints.md

@@ -549,7 +549,19 @@ You will typically select this workflow when:
 
 Use the following steps to deploy an MLflow model with a custom scoring script.
 
-1. Create a scoring script:
+1. Identify the folder where your MLflow model is placed.
+
+    a. Go to [Azure Machine Learning portal](https://ml.azure.com).
+
+    b. Go to the section __Models__.
+
+    c. Select the model you are trying to deploy and click on the tab __Artifacts__.
+
+    d. Take note of the folder that is displayed. This folder was indicated when the model was registered.
+
+    :::image type="content" source="media/how-to-deploy-mlflow-models-online-endpoints/mlflow-model-folder-name.png" lightbox="media/how-to-deploy-mlflow-models-online-endpoints/mlflow-model-folder-name.png" alt-text="Screenshot showing the folder where the model artifacts are placed.":::
+
+1. Create a scoring script. Notice how the folder name `model` you identified before has been included in the `init()` function.
 
     __score.py__
 

articles/machine-learning/how-to-mlflow-batch.md

@@ -471,7 +471,19 @@ You will typically select this workflow when:
 
 Use the following steps to deploy an MLflow model with a custom scoring script.
 
-1. Create a scoring script:
+1. Identify the folder where your MLflow model is placed.
+
+    a. Go to [Azure Machine Learning portal](https://ml.azure.com).
+
+    b. Go to the section __Models__.
+
+    c. Select the model you are trying to deploy and click on the tab __Artifacts__.
+
+    d. Take note of the folder that is displayed. This folder was indicated when the model was registered.
+
+    :::image type="content" source="media/how-to-deploy-mlflow-models-online-endpoints/mlflow-model-folder-name.png" lightbox="media/how-to-deploy-mlflow-models-online-endpoints/mlflow-model-folder-name.png" alt-text="Screenshot showing the folder where the model artifacts are placed.":::
+
+1. Create a scoring script. Notice how the folder name `model` you identified before has been included in the `init()` function.
 
    __batch_driver.py__