Merge pull request #89246 from nibaccam/adb-mlflo

MLflow | Azure Databricks integration part 2

Author: PRMerger7

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 +180,18 @@ 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
+
+On the [Azure portal](https://ms.portal.azure.com), you can link your Azure Databricks (ADB) workspace to a new or existing Azure Machine Learning workspace. To do so, navigate to your ADB workspace and select the **Link Azure Machine Learning workspace** button on the bottom right. 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.
 
+#### Directly set MLflow Tracking in your notebook
+
 ```python
 uri = ws.get_mlflow_tracking_uri()
 mlflow.set_tracking_uri(uri)
@@ -196,6 +204,12 @@ import mlflow
 mlflow.log_metric('epoch_loss', loss.item()) 
 ```
 
+#### Automate setting MLflow Tracking
+
+Instead of manually setting the tracking URI in every subsequent experiment notebook session on your clusters, do so automatically 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).
+
+When configured correctly, you are able to see your MLflow tracking data in Azure Machine Learning's REST API and all clients, and in Azure Databricks via the MLflow user interface or by 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.