Merge branch 'master' of github.com:MicrosoftDocs/azure-docs-pr

Author: Duncanma

articles/backup/backup-azure-vms-automation.md

@@ -316,9 +316,9 @@ Set-AzRecoveryServicesBackupProtectionPolicy -Policy $pol  -RetentionPolicy $Ret
 > From Az PS version 1.6.0 onwards, one can update the instant restore snapshot retention period in policy using Powershell
 
 ````powershell
-$bkpPol = Get-AzureRmRecoveryServicesBackupProtectionPolicy -WorkloadType "AzureVM" -VaultId $targetVault.ID
+$bkpPol = Get-AzRecoveryServicesBackupProtectionPolicy -WorkloadType "AzureVM" -VaultId $targetVault.ID
 $bkpPol.SnapshotRetentionInDays=7
-Set-AzureRmRecoveryServicesBackupProtectionPolicy -policy $bkpPol -VaultId $targetVault.ID
+Set-AzRecoveryServicesBackupProtectionPolicy -policy $bkpPol -VaultId $targetVault.ID
 ````
 
 The default value will be 2, user can set the value with a min of 1 and max of 5. For weekly backup policies, the period is set to 5 and cannot be changed.

articles/billing/billing-understand-reserved-instance-usage-ea.md

@@ -58,14 +58,14 @@ Other information available in Azure usage data has changed:
 
 You can get the data using the API or download it from Azure portal.
 
-You call the [Usage Details API](/rest/api/consumption/usagedetails/list) with API version "2019-04-01-preview" to get the new data. For details about terminology, see [usage terms](billing-understand-your-usage.md). The caller should be an Enterprise Administrator for the enterprise agreement using the [EA portal](https://ea.azure.com). Read-only Enterprise Administrators can also get the data.
+You call the [Usage Details API](/rest/api/consumption/usagedetails/list) to get the new data. For details about terminology, see [usage terms](billing-understand-your-usage.md). The caller should be an Enterprise Administrator for the enterprise agreement using the [EA portal](https://ea.azure.com). Read-only Enterprise Administrators can also get the data.
 
 The data is not available in [Reporting APIs for Enterprise customers - Usage Details](/rest/api/billing/enterprise/billing-enterprise-api-usage-detail).
 
 Here's an example call to the API:
 
 ```
-https://management.azure.com/providers/Microsoft.Billing/billingAccounts/{enrollmentId}/providers/Microsoft.Billing/billingPeriods/{billingPeriodId}/providers/Microsoft.Consumption/usagedetails?metric={metric}&api-version=2019-04-01-preview&$filter={filter}
+https://management.azure.com/providers/Microsoft.Billing/billingAccounts/{enrollmentId}/providers/Microsoft.Billing/billingPeriods/{billingPeriodId}/providers/Microsoft.Consumption/usagedetails?metric={metric}&api-version=2019-05-01&$filter={filter}
 ```
 
 For more information about {enrollmentId} and {billingPeriodId}, see the [Usage Details – List](https://docs.microsoft.com/rest/api/consumption/usagedetails/list) API article.

articles/event-hubs/event-hubs-java-get-started-send.md

@@ -178,11 +178,11 @@ To use EventProcessorHost, you must have an [Azure Storage account][Azure Storag
 1. Sign in the [Azure portal](https://portal.azure.com), and select **Create a resource** on the left-hand side of the screen.
 2. Select **Storage**, then select **Storage account**. In the **Create storage account** window, type a name for the storage account. Complete the rest of the fields, select your desired region, and then select **Create**.
 
-    ![Create storage account](./media/event-hubs-dotnet-framework-getstarted-receive-eph/create-storage2.png)
+    ![Create a storage account in Azure portal](./media/event-hubs-dotnet-framework-getstarted-receive-eph/create-azure-storage-account.png)
 
 3. Select the newly created storage account, and then select **Access Keys**:
 
-    ![Get access keys](./media/event-hubs-dotnet-framework-getstarted-receive-eph/create-storage3.png)
+    ![Get your access keys in Azure portal](./media/event-hubs-dotnet-framework-getstarted-receive-eph/select-azure-storage-access-keys.png)
 
     Copy the key1 value to a temporary location. You use it later in this tutorial.
 

articles/event-hubs/media/event-hubs-dotnet-framework-getstarted-receive-eph/create-storage2.png renamed to articles/event-hubs/media/event-hubs-dotnet-framework-getstarted-receive-eph/create-azure-storage-account.png

@@ -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.