connect workspaces & helper script

Author: nibaccam

articles/machine-learning/service/how-to-use-mlflow.md

@@ -9,7 +9,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.reviewer: nibaccam
 ms.topic: conceptual
-ms.date: 08/07/2019
+ms.date: 09/23/2019
 ms.custom: seodec18
 ---
 
@@ -141,6 +141,7 @@ MLflow Tracking with Azure Machine Learning lets you store the logged metrics an
 To run your Mlflow experiments with Azure Databricks, you need to first create an [Azure Databricks workspace and cluster](https://docs.microsoft.com/azure/azure-databricks/quickstart-create-databricks-workspace-portal)
 
 In your cluster, be sure to install the *azureml-mlflow* library from PyPi, to ensure that your cluster has access to the necessary functions and classes.
+From here, import your experiment notebook, attach your cluster to it and run your experiment. 
 
 ### Install libraries
 
@@ -179,11 +180,17 @@ workspace_name = 'workspace_name'
 ws = Workspace.get(name=workspace_name,
                    subscription_id=subscription_id,
                    resource_group=resource_group)
-
 ```
+
+#### Connect your Azure Databricks and Azure Machine Learning workspaces
+
+You also have the option to link your ADB workspace to your Azure Machine Learning workspace at the click of a button on the Azure Databricks (ADB) Azure portal interface. Linking your workspaces enables you to track your experiment data in the Azure Machine Learning workspace. 
+
 ### Link MLflow tracking to your workspace
+
 After you instantiate your workspace, set the MLflow tracking URI. By doing so, you link the MLflow tracking to Azure Machine Learning workspace. After this, all your experiments will land in the managed Azure Machine Learning tracking service.
 
+
 ```python
 uri = ws.get_mlflow_tracking_uri()
 mlflow.set_tracking_uri(uri)
@@ -196,6 +203,10 @@ import mlflow
 mlflow.log_metric('epoch_loss', loss.item()) 
 ```
 
+You also can automatically configure the MLflow tracking URI on your clusters for all subsequent notebook sessions using this [Azure Machine Learning Tracking Cluster Init script](https://github.com/Azure/MachineLearningNotebooks/blob/3ce779063b000e0670bdd1acc6bc3a4ee707ec13/how-to-use-azureml/azure-databricks/linking/README.md) instead of manually setting the tracking URI in  your experiment notebook.
+
+When configured correctly, you'll be able to see your MLflow tracking data in Azure Machine Learning's REST API and all clients, and Azure Databricks via the MLflow user interface and using the MLflow client.
+
 ## View metrics and artifacts in your workspace
 
 The metrics and artifacts from MLflow logging are kept in your workspace. To view them anytime, navigate to your workspace and find the experiment by name on the [Azure portal](https://portal.azure.com) or in your [workspace landing page (preview)](https://ml.azure.com).  Or run the below code.