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

commit

Author: asudbring

articles/active-directory-b2c/restful-technical-profile.md

@@ -9,7 +9,7 @@ manager: celestedg
 ms.service: active-directory
 ms.workload: identity
 ms.topic: reference
-ms.date: 02/24/2020
+ms.date: 03/02/2020
 ms.author: mimart
 ms.subservice: B2C
 ---
@@ -122,6 +122,7 @@ The technical profile also returns claims, that aren't returned by the identity
 | --------- | -------- | ----------- |
 | ServiceUrl | Yes | The URL of the REST API endpoint. |
 | AuthenticationType | Yes | The type of authentication being performed by the RESTful claims provider. Possible values: `None`, `Basic`, `Bearer`, or `ClientCertificate`. The `None` value indicates that the REST API is not anonymous. The `Basic` value indicates that the REST API is secured with HTTP basic authentication. Only verified users, including Azure AD B2C, can access your API. The `ClientCertificate` (recommended) value indicates that the REST API restricts access by using client certificate authentication. Only services that have the appropriate certificates, for example Azure AD B2C, can access your API. The `Bearer` value indicates that the REST API restricts access using client OAuth2 Bearer token. |
+| AllowInsecureAuthInProduction| No| Indicates whether the `AuthenticationType` can be set to `none` in production environment (`DeploymentMode` of the [TrustFrameworkPolicy](trustframeworkpolicy.md) is set to `Production`, or not specified). Possible values: true, or false (default). |
 | SendClaimsIn | No | Specifies how the input claims are sent to the RESTful claims provider. Possible values: `Body` (default), `Form`, `Header`, or `QueryString`. The `Body` value is the input claim that is sent in the request body in JSON format. The `Form` value is the input claim that is sent in the request body in an ampersand '&' separated key value format. The `Header` value is the input claim that is sent in the request header. The `QueryString` value is the input claim that is sent in the request query string. The HTTP verbs invoked by each are as follows:<br /><ul><li>`Body`: POST</li><li>`Form`: POST</li><li>`Header`: GET</li><li>`QueryString`: GET</li></ul> |
 | ClaimsFormat | No | Specifies the format for the output claims. Possible values: `Body` (default), `Form`, `Header`, or `QueryString`. The `Body` value is the output claim that is sent in the request body in JSON format. The `Form` value is the output claim that is sent in the request body in an ampersand '&' separated key value format. The `Header` value is the output claim that is sent in the request header. The `QueryString` value is the output claim that is sent in the request query string. |
 | ClaimUsedForRequestPayload| No | Name of a string claim that contains the payload to be sent to the REST API. |

articles/azure-resource-manager/templates/deploy-to-management-group.md

@@ -2,15 +2,13 @@
 title: Deploy resources to management group
 description: Describes how to deploy resources at the management group scope in an Azure Resource Manager template.
 ms.topic: conceptual
-ms.date: 02/10/2020
+ms.date: 03/02/2020
 ---
 
 # Create resources at the management group level
 
 Typically, you deploy Azure resources to a resource group in your Azure subscription. However, you can also create resources at the management group level. You use management group level deployments to take actions that make sense at that level, such as assigning [role-based access control](../../role-based-access-control/overview.md) or applying [policies](../../governance/policy/overview.md).
 
-Currently, to deploy templates at the management group level, you must use the REST API.
-
 ## Supported resources
 
 You can deploy the following resource types at the management group level:
@@ -40,7 +38,16 @@ https://schema.management.azure.com/schemas/2019-08-01/managementGroupDeployment
 
 ## Deployment commands
 
-The command for management group deployments is different than the command for resource group deployments.
+The commands for management group deployments are different than the commands for resource group deployments.
+
+For Azure PowerShell, use [New-AzManagementGroupDeployment](/powershell/module/az.resources/new-azmanagementgroupdeployment). 
+
+```azurepowershell-interactive
+New-AzManagementGroupDeployment `
+  -ManagementGroupId "myMG" `
+  -Location "West US" `
+  -TemplateUri https://raw.githubusercontent.com/Azure/azure-docs-json-samples/master/management-level-deployment/azuredeploy.json
+```
 
 For REST API, use [Deployments - Create At Management Group Scope](/rest/api/resources/deployments/createorupdateatmanagementgroupscope).
 
@@ -145,7 +152,7 @@ The following example assigns an existing policy definition to the management gr
 
 ## Template sample
 
-* Create a resource group, a policy and a policy assignment.  See [here](https://github.com/Azure/azure-docs-json-samples/blob/master/management-level-deployment/azuredeploy.json).
+* [Create a resource group, a policy and a policy assignment](https://github.com/Azure/azure-docs-json-samples/blob/master/management-level-deployment/azuredeploy.json).
 
 ## Next steps
 

articles/azure-resource-manager/templates/deploy-to-subscription.md

@@ -2,7 +2,7 @@
 title: Deploy resources to subscription
 description: Describes how to create a resource group in an Azure Resource Manager template. It also shows how to deploy resources at the Azure subscription scope.
 ms.topic: conceptual
-ms.date: 02/10/2020
+ms.date: 03/02/2020
 ---
 
 # Create resource groups and resources at the subscription level
@@ -15,6 +15,7 @@ To deploy templates at the subscription level, use Azure CLI, PowerShell, or RES
 
 You can deploy the following resource types at the subscription level:
 
+* [budgets](/azure/templates/microsoft.consumption/budgets)
 * [deployments](/azure/templates/microsoft.resources/deployments)
 * [peerAsns](/azure/templates/microsoft.peering/peerasns)
 * [policyAssignments](/azure/templates/microsoft.authorization/policyassignments)
@@ -55,10 +56,10 @@ az deployment create \
 ```
 
 
-For the PowerShell deployment command, use [New-AzDeployment](/powershell/module/az.resources/new-azdeployment). The following example deploys a template to create a resource group:
+For the PowerShell deployment command, use [New-AzDeployment](/powershell/module/az.resources/new-azdeployment) or **New-AzSubscriptionDeployment**. The following example deploys a template to create a resource group:
 
 ```azurepowershell-interactive
-New-AzDeployment `
+New-AzSubscriptionDeployment `
   -Name demoDeployment `
   -Location centralus `
   -TemplateUri https://raw.githubusercontent.com/Azure/azure-docs-json-samples/master/azure-resource-manager/emptyRG.json `
@@ -295,7 +296,7 @@ $definition = Get-AzPolicyDefinition | Where-Object { $_.Properties.DisplayName
 $locations = @("westus", "westus2")
 $policyParams =@{listOfAllowedLocations = @{ value = $locations}}
 
-New-AzDeployment `
+New-AzSubscriptionDeployment `
   -Name policyassign `
   -Location centralus `
   -TemplateUri https://raw.githubusercontent.com/Azure/azure-docs-json-samples/master/azure-resource-manager/policyassign.json `
@@ -361,16 +362,16 @@ az deployment create \
 To deploy this template with PowerShell, use:
 
 ```azurepowershell
-New-AzDeployment `
+New-AzSubscriptionDeployment `
   -Name definePolicy `
   -Location centralus `
   -TemplateUri https://raw.githubusercontent.com/Azure/azure-docs-json-samples/master/azure-resource-manager/policydefineandassign.json
 ```
 
 ## Template samples
 
-* Create a resource group, lock it and give permissions to it. See [here](https://github.com/Azure/azure-quickstart-templates/tree/master/subscription-level-deployments/create-rg-lock-role-assignment).
-* Create a resource group, a policy and a policy assignment.  See [here](https://github.com/Azure/azure-docs-json-samples/blob/master/subscription-level-deployment/azuredeploy.json).
+* [Create a resource group, lock it and give permissions to it](https://github.com/Azure/azure-quickstart-templates/tree/master/subscription-level-deployments/create-rg-lock-role-assignment).
+* [Create a resource group, a policy, and a policy assignment](https://github.com/Azure/azure-docs-json-samples/blob/master/subscription-level-deployment/azuredeploy.json).
 
 ## Next steps
 

articles/fxt-edge-filer/fxt-specs.md

@@ -3,7 +3,7 @@ title: Microsoft Azure FXT Edge Filer specifications | Microsoft Docs
 description: Physical and environmental specifications for Azure FXT Edge Filer hardware
 author: ekpgh
 ms.service: fxt-edge-filer
-ms.topic: overview
+ms.topic: conceptual
 ms.date: 06/20/2019
 ms.author: rohogue
 ---

articles/machine-learning/how-to-designer-python.md

@@ -0,0 +1,83 @@
+---
+title: Python
+titleSuffix: Azure Machine Learning
+description: Learn how to use Python in Azure Machine Learning designer to transform data.
+services: machine-learning
+ms.service: machine-learning
+ms.subservice: core
+ms.topic: how-to
+
+author: peterclu
+ms.author: peterlu
+ms.date: 02/28/2020
+---
+
+# Execute Python code in Azure Machine Learning designer
+
+In this article, you learn how to use the [Execute Python Script](algorithm-module-reference/execute-python-script.md) module to add custom logic to  Azure Machine Learning designer. In the following how-to, you use the Pandas library to do simple feature engineering.
+
+You can use the in-built code editor to quickly add simple Python logic. If you want to add more complex code or upload additional Python libraries, you should use the zip file method.
+
+The default execution environment uses the Anacondas distribution of Python. For a complete list of pre-installed packages, see the [Execute Python Script module reference](algorithm-module-reference/execute-python-script.md) page.
+
+![Execute Python input map](media/how-to-designer-python/execute-python-map.png)
+
+## Execute Python written in the designer
+
+### Add the Execute Python Script module
+
+1. Find the **Execute Python Script** module in the designer palette. It can be found in the **Python Language** section.
+
+1. Drag and drop the module onto the pipeline canvas.
+
+### Connect input datasets
+
+This article uses the sample dataset, **Automobile price data (Raw)**. 
+
+1. Drag and drop your dataset to the pipeline canvas.
+
+1. Connect the output port of the dataset to the top-left input port of the **Execute Python Script** module. The designer exposes the input as a parameter to the entry point script.
+    
+    The right input port is reserved for zipped python libraries.
+
+    ![Connect datasets](media/how-to-designer-python/connect-dataset.png)
+        
+
+1. Take note of which input port you use. The designer assigns the left input port to the variable `dataset1` and the middle input port to `dataset2`. 
+
+Input modules are optional since you can generate or import data directly in the **Execute Python Script** module.
+
+### Write your Python code
+
+The designer provides an initial entry point script for you to edit and enter your own Python code. 
+
+In this example, you use Pandas to combine two columns found in the automobile dataset, **Price** and **Horsepower**, to create a new column, **Dollars per horsepower**. This column represents how much you pay for each horsepower, which could be a useful feature to decide if a car is a good deal for the money. 
+
+1. Select the **Execute Python Script** module.
+
+1. In the pane that appears to the right of the canvas, select the **Python script** text box.
+
+1. Copy and paste the following code into the text box.
+
+    ```python
+    import pandas as pd
+    
+    def azureml_main(dataframe1 = None, dataframe2 = None):
+        dataframe1['Dollar/HP'] = dataframe1.price / dataframe1.horsepower
+        return dataframe1
+    ```
+    Your pipeline should look the following image:
+    
+    ![Execute Python pipeline](media/how-to-designer-python/execute-python-pipeline.png)
+
+    The entry point script must contain the function `azureml_main`. There are two function parameters that map to the two input ports for the **Execute Python Script** module.
+
+    The return value must be a Pandas Dataframe. You can return up to two dataframes as module outputs.
+    
+1. Run the pipeline.
+
+Now, you have a dataset with the new feature **Dollars/HP**, which could be useful in training a car recommender. This is an example of feature extraction and dimensionality reduction. 
+
+## Next steps
+
+Learn how to [import your own data](how-to-designer-import-data.md) in Azure Machine Learning designer.

articles/machine-learning/media/how-to-designer-python/connect-dataset.png

@@ -367,18 +367,21 @@
     - name: 'Azure Pipelines for CI/CD'
       displayName: continuous, integration, delivery
       href: /azure/devops/pipelines/targets/azure-machine-learning?context=azure/machine-learning/service/context/ml-context
-    - name: 'Designer: Retrain using published pipelines'
+    - name: 'Designer retrain using published pipelines'
       displayName: retrain, designer, published pipeline
       href: how-to-retrain-designer.md
+    - name: Designer batch predictions
+      displayName: score scoring asynchronous consume pipeline parallelrunstep inference designer
+      href: how-to-run-batch-predictions-designer.md
+    - name: Designer execute Python code
+      displayName: feature extraction, feature engineering
+      href: how-to-designer-python.md
     - name: Use parallel run step
       displayName: score scoring batch consume pipeline parallelrunstep inference
       href: how-to-use-parallel-run-step.md
     - name: Debug & troubleshoot parallel run step
       displayName: debug_batch consume pipeline parallelrunstep inference
       href: how-to-debug-parallel-run-step.md
-    - name: Designer batch predictions
-      displayName: score scoring asynchronous consume pipeline parallelrunstep inference designer
-      href: how-to-run-batch-predictions-designer.md
   - name: Manage resource quotas
     displayName: limits
     href: how-to-manage-quotas.md