Merge pull request #95088 from lobrien/laobri/162184-content-review

Content review: Screenshot update (scheduling pipelines) and minor edits

Author: v-albemi

articles/machine-learning/service/concept-ml-pipelines.md

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: laobri
 author: lobrien
-ms.date: 11/04/2019
+ms.date: 11/06/2019
 ---
 
 # What are Azure Machine Learning pipelines?

articles/machine-learning/service/how-to-create-labeling-projects.md

@@ -14,7 +14,7 @@ ms.date: 11/04/2019
 
 Labeling large amounts of data has often been a headache in machine learning projects. ML projects with a computer vision component, such as image classification or object detection, generally require thousands of images and corresponding labels. 
 
-Azure Machine Learning studio gives you a central location to create, manage, and monitor labeling projects. Labeling projects help coordinate the data, labels, and team members, allowing you to more efficiently manage the labeling tasks. Currently supported tasks are image classification, either multi-label or multi-class, and object identification using bounded boxes.
+Azure Machine Learning gives you a central location to create, manage, and monitor labeling projects. Labeling projects help coordinate the data, labels, and team members, allowing you to more efficiently manage the labeling tasks. Currently supported tasks are image classification, either multi-label or multi-class, and object identification using bounded boxes.
 
 Azure tracks progress and maintains the queue of incomplete labeling tasks. Labelers don't require an Azure account to participate. Once authenticated with their Microsoft Account (MSA) or [Azure Active Directory](https://docs.microsoft.com/azure/active-directory/active-directory-whatis), they can do as much or as little labeling as their time allows. They can assign and change labels using keyboard shortcuts. 
 
@@ -39,7 +39,7 @@ In this article, you'll learn how to:
 
 ## Create a labeling project
 
-Labeling projects are administered from [Azure Machine Learning studio](https://ml.azure.com/). The **Labeling projects** page allows you to manage your projects, teams, and people. A project has one or more teams assigned to it, and a team has one or more people assigned to it. 
+Labeling projects are administered from [Azure Machine Learning](https://ml.azure.com/). The **Labeling projects** page allows you to manage your projects, teams, and people. A project has one or more teams assigned to it, and a team has one or more people assigned to it. 
 
 If your data are already stored in Azure blob storage, you should make them available as a datastore before creating your labeling project. For information, see [Create and register datastores](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data#create-and-register-datastores). 
 
@@ -145,7 +145,7 @@ You can label data directly from the **Project details** page by selecting **Lab
 
 At any time, you may export the label data for machine learning experimentation. Image labels can be exported in [COCO format](http://cocodataset.org/#format-data) or as an Azure ML dataset. You will find the **Export** button on the **Project details** page of your labeling project.
 
-The COCO file is created in the default blob store of the Azure ML workspace in a folder within **export/coco**. You can access the exported Azure ML dataset under the **Datasets** section of studio. Dataset details page also provides sample code to access your labels from Python.
+The COCO file is created in the default blob store of the Azure ML workspace in a folder within **export/coco**. You can access the exported Azure ML dataset under the **Datasets** section of Azure Machine Learning. Dataset details page also provides sample code to access your labels from Python.
 
 ![Exported dataset](media/how-to-create-labeling-projects/exported-dataset.png)
 

articles/machine-learning/service/how-to-create-your-first-pipeline.md

@@ -407,14 +407,14 @@ response = requests.post(published_pipeline1.endpoint,
 ### View results of a published pipeline
 
 See the list of all your published pipelines and their run details:
-1. Sign in to [Azure Machine Learning studio](https://ml.azure.com).
+1. Sign in to [Azure Machine Learning](https://ml.azure.com).
 
 1. [View your workspace](how-to-manage-workspace.md#view) to find the list of pipelines.
  ![list of machine learning pipelines](./media/how-to-create-your-first-pipeline/list_of_pipelines.png)
 
 1. Select a specific pipeline to see the run results.
 
-These results are also available in your workspace in [Azure Machine Learning studio]](https://ml.azure.com).
+These results are also available in your workspace in [Azure Machine Learning](https://ml.azure.com).
 
 ### Disable a published pipeline
 

articles/machine-learning/service/how-to-enable-app-insights.md

@@ -17,19 +17,20 @@ ms.custom: seoapril2019
 [!INCLUDE [applies-to-skus](../../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
 In this article, you learn how to collect data from and monitor models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) by enabling Azure Application Insights. In addition to collecting an endpoint's input data and response, you can monitor:
-* Request rates, response times, and failure rates.
-* Dependency rates, response times, and failure rates.
-* Exceptions.
+
+* Request rates, response times, and failure rates
+* Dependency rates, response times, and failure rates
+* Exceptions
 
 [Learn more about Azure Application Insights](../../azure-monitor/app/app-insights-overview.md). 
 
 
 ## Prerequisites
 
-* If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
+* If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today
 
-* An Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed. To learn how to get these prerequisites, see [How to configure a development environment](how-to-configure-environment.md).
-* A trained machine learning model to be deployed to Azure Kubernetes Service (AKS) or Azure Container Instance (ACI). If you don't have one, see the [Train image classification model](tutorial-train-models-with-aml.md) tutorial.
+* An Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed. To learn how to get these prerequisites, see [How to configure a development environment](how-to-configure-environment.md)
+* A trained machine learning model to be deployed to Azure Kubernetes Service (AKS) or Azure Container Instance (ACI). If you don't have one, see the [Train image classification model](tutorial-train-models-with-aml.md) tutorial
 
 ## Web service input and response data
 
@@ -39,65 +40,68 @@ The input and response to the service - corresponding to the inputs to the ML mo
 
 You can enable and disable Azure Application Insights in the Azure portal. 
 
-1. In the [Azure portal](https://portal.azure.com), open your workspace.
+1. In the [Azure portal](https://portal.azure.com), open your workspace
 
-1. On the **Deployments** tab, select the service where you want to enable Azure Application Insights.
+1. On the **Deployments** tab, select the service where you want to enable Azure Application Insights
 
    [![List of services on the Deployments tab](media/how-to-enable-app-insights/Deployments.PNG)](./media/how-to-enable-app-insights/Deployments.PNG#lightbox)
 
-3. Select **Edit**.
+3. Select **Edit**
 
    [![Edit button](media/how-to-enable-app-insights/Edit.PNG)](./media/how-to-enable-app-insights/Edit.PNG#lightbox)
 
-4. In **Advanced Settings**, select the **Enable AppInsights diagnostics** check box.
+4. In **Advanced Settings**, select the **Enable AppInsights diagnostics** check box
 
    [![Selected check box for enabling diagnostics](media/how-to-enable-app-insights/AdvancedSettings.png)](./media/how-to-enable-app-insights/AdvancedSettings.png#lightbox)
 
-1. Select **Update** at the bottom of the screen to apply the changes. 
+1. Select **Update** at the bottom of the screen to apply the changes
 
 ### Disable
-1. In the [Azure portal](https://portal.azure.com), open your workspace.
-1. Select **Deployments**, select the service, and then select **Edit**.
+
+1. In the [Azure portal](https://portal.azure.com), open your workspace
+1. Select **Deployments**, select the service, and then select **Edit**
 
    [![Use the edit button](media/how-to-enable-app-insights/Edit.PNG)](./media/how-to-enable-app-insights/Edit.PNG#lightbox)
 
-1. In **Advanced Settings**, clear the **Enable AppInsights diagnostics** check box. 
+1. In **Advanced Settings**, clear the **Enable AppInsights diagnostics** check box
 
    [![Cleared check box for enabling diagnostics](media/how-to-enable-app-insights/uncheck.png)](./media/how-to-enable-app-insights/uncheck.png#lightbox)
 
-1. Select **Update** at the bottom of the screen to apply the changes. 
+1. Select **Update** at the bottom of the screen to apply the changes
 
 ## Use Python SDK to configure 
 
 ### Update a deployed service
-1. Identify the service in your workspace. The value for `ws` is the name of your workspace.
+
+1. Identify the service in your workspace. The value for `ws` is the name of your workspace
 
     ```python
     from azureml.core.webservice import Webservice
     aks_service= Webservice(ws, "my-service-name")
     ```
-2. Update your service and enable Azure Application Insights. 
+2. Update your service and enable Azure Application Insights
 
     ```python
     aks_service.update(enable_app_insights=True)
     ```
 
 ### Log custom traces in your service
+
 If you want to log custom traces, follow the standard deployment process for AKS or ACI in the [How to deploy and where](how-to-deploy-and-where.md) document. Then use the following steps:
 
-1. Update the scoring file by adding print statements.
+1. Update the scoring file by adding print statements
 
     ```python
     print ("model initialized" + time.strftime("%H:%M:%S"))
     ```
 
-2. Update the service configuration.
+2. Update the service configuration
 
     ```python
     config = Webservice.deploy_configuration(enable_app_insights=True)
     ```
 
-3. Build an image and deploy it on [AKS](how-to-deploy-to-aks.md) or [ACI](how-to-deploy-to-aci.md).  
+3. Build an image and deploy it on [AKS](how-to-deploy-to-aks.md) or [ACI](how-to-deploy-to-aci.md)
 
 ### Disable tracking in Python
 
@@ -111,22 +115,23 @@ To disable Azure Application Insights, use the following code:
 ## Evaluate data
 Your service's data is stored in your Azure Application Insights account, within the same resource group as Azure Machine Learning.
 To view it:
-1. Go to your Machine Learning service workspace in [Azure Machine Learning studio](https://ml.azure.com) and click on Application Insights link.
+
+1. Go to your Machine Learning service workspace in [Azure Machine Learning studio](https://ml.azure.com) and click on Application Insights link
 
     [![AppInsightsLoc](media/how-to-enable-app-insights/AppInsightsLoc.png)](./media/how-to-enable-app-insights/AppInsightsLoc.png#lightbox)
 
-1. Select the **Overview** tab to see a basic set of metrics for your service.
+1. Select the **Overview** tab to see a basic set of metrics for your service
 
    [![Overview](media/how-to-enable-app-insights/overview.png)](./media/how-to-enable-app-insights/overview.png#lightbox)
 
 1. To look into your web service input and response payloads, select **Analytics**
-1. In the schema section, select **Traces** and filter down traces with the message `"model_data_collection"`. In the custom dimensions, you can see the inputs, predictions, and other relevant details.
+1. In the schema section, select **Traces** and filter down traces with the message `"model_data_collection"`. In the custom dimensions, you can see the inputs, predictions, and other relevant details
 
    [![Model data](media/how-to-enable-app-insights/model-data-trace.png)](./media/how-to-enable-app-insights/model-data-trace.png#lightbox)
 
 
-3. To look into your custom traces, select **Analytics**.
-4. In the schema section, select **Traces**. Then select **Run** to run your query. Data should appear in a table format and should map to your custom calls in your scoring file. 
+3. To look into your custom traces, select **Analytics**
+4. In the schema section, select **Traces**. Then select **Run** to run your query. Data should appear in a table format and should map to your custom calls in your scoring file
 
    [![Custom traces](media/how-to-enable-app-insights/logs.png)](./media/how-to-enable-app-insights/logs.png#lightbox)
 
@@ -147,5 +152,5 @@ The [enable-app-insights-in-production-service.ipynb](https://github.com/Azure/M
 
 ## Next steps
 
-* See [how to deploy a model to an Azure Kubernetes Service cluster](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-azure-kubernetes-service) or [how to deploy a model to Azure Container Instances](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-azure-container-instance) to deploy your models to web service endpoints, and enable Azure Application Insights to leverage data collection and endpoint monitoring.
-* See [MLOps: Manage, deploy, and monitor models with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/concept-model-management-and-deployment) to learn more about leveraging data collected from models in production. Such data can help to continually improve your machine learning process. 
+* See [how to deploy a model to an Azure Kubernetes Service cluster](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-azure-kubernetes-service) or [how to deploy a model to Azure Container Instances](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-azure-container-instance) to deploy your models to web service endpoints, and enable Azure Application Insights to leverage data collection and endpoint monitoring
+* See [MLOps: Manage, deploy, and monitor models with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/concept-model-management-and-deployment) to learn more about leveraging data collected from models in production. Such data can help to continually improve your machine learning process