rebranding

articles/iot-edge/tutorial-machine-learning-edge-05-configure-edge-device.md

@@ -68,11 +68,11 @@ In this section, we create the self-signed certificates using a Docker image tha
 
 ## Upload certificates to Azure Key Vault
 
-To store our certificates securely and to make them accessible from multiple devices, we will upload the certificates into Azure Key Vault. As you can see from the list above, we have two types of certificate files: PFX and PEM. We will treat the PFX as Key Vault Certificates to be uploaded to Key Vault. The PEM files are plain text and we will treat them as Key Vault Secrets. We will use the Key Vault associated with the Azure Machine Learning service workspace we created by running the [Azure Notebooks](tutorial-machine-learning-edge-04-train-model.md#run-azure-notebooks).
+To store our certificates securely and to make them accessible from multiple devices, we will upload the certificates into Azure Key Vault. As you can see from the list above, we have two types of certificate files: PFX and PEM. We will treat the PFX as Key Vault Certificates to be uploaded to Key Vault. The PEM files are plain text and we will treat them as Key Vault Secrets. We will use the Key Vault associated with the Azure Machine Learning workspace we created by running the [Azure Notebooks](tutorial-machine-learning-edge-04-train-model.md#run-azure-notebooks).
 
-1. From the [Azure portal](https://portal.azure.com), navigate to your Azure Machine Learning service workspace.
+1. From the [Azure portal](https://portal.azure.com), navigate to your Azure Machine Learning workspace.
 
-2. From the overview page of the Azure Machine Learning service workspace, find the name of the **Key Vault**.
+2. From the overview page of the Azure Machine Learning workspace, find the name of the **Key Vault**.
 
     ![Copy key vault name](media/tutorial-machine-learning-edge-05-configure-edge-device/find-key-vault-name.png)
 

articles/iot-edge/tutorial-machine-learning-edge-06-custom-modules.md

@@ -167,7 +167,7 @@ Next, we add the Router module to our solution. The Router module handles severa
 4. When prompted for your Docker Image Repository, use the registry from the machine learning workspace (you can find the registry in the registryCredentials node of your *deployment.template.json* file). This value is the fully qualified address to the registry, like **\<your registry\>.azurecr.io/turbofanrouter**.
 
     > [!NOTE]
-    > In this article, we use the Azure Container Registry created by the Azure Machine Learning service workspace, which we used to train and deploy our classifier. This is purely for convenience. We could have created a new container registry and published our modules there.
+    > In this article, we use the Azure Container Registry created by the Azure Machine Learning  workspace, which we used to train and deploy our classifier. This is purely for convenience. We could have created a new container registry and published our modules there.
 
 5. Open a new terminal window in Visual Studio Code (**View** > **Terminal**) and copy files from the modules directory.
 
@@ -749,7 +749,7 @@ Now that we have made the configuration changes, we are ready to build the image
 
 Once the build successfully completes, we will be able to use the Azure portal to review our published modules.
 
-1. In the Azure portal, navigate to your Azure Machine Learning service workspace and click the hyperlink for **Registry**.
+1. In the Azure portal, navigate to your Azure Machine Learning workspace and click the hyperlink for **Registry**.
 
     ![Navigate to registry from machine learning service workspace](media/tutorial-machine-learning-edge-06-custom-modules/follow-registry-link.png)
 

articles/notebooks/use-machine-learning-services-jupyter-notebooks.md

@@ -39,7 +39,7 @@ Azure Notebooks comes pre-configured with the necessary environment to work with
 
 ## Next steps
 
-The Azure Machine Learning Services documentation contains a variety of other resources that guide you through working with Machine Learning Service within notebooks:
+The Azure Machine Learning documentation contains a variety of other resources that guide you through working with Machine Learning within notebooks:
 
 - [Quickstart: Use Python to get started with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/quickstart-create-workspace-with-python)
 - [Tutorial #1: Train an image classification model with Azure Machine Learning service](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml)

articles/open-datasets/tutorial-opendatasets-automl.md

@@ -17,7 +17,7 @@ In this tutorial, you leverage the convenience of Azure Open Datasets along with
 
 In this tutorial you learn the following tasks:
 
-- Configure an Azure Machine Learning service workspace
+- Configure an Azure Machine Learning workspace
 - Set up a local Python environment
 - Access, transform, and join data using Azure Open Datasets
 - Train an automated machine learning regression model
@@ -27,7 +27,7 @@ In this tutorial you learn the following tasks:
 
 This tutorial requires the following prerequisites.
 
-* An Azure Machine Learning service workspace
+* An Azure Machine Learning workspace
 * A Python 3.6 environment
 
 ### Create a workspace
@@ -1795,7 +1795,7 @@ X_train, X_test, y_train, y_test = train_test_split(
 
 ### Load workspace and configure experiment
 
-Load your Azure Machine Learning service workspace using the `get()` function with your subscription and workspace information. Create an experiment within your workspace to store and monitor your model runs.
+Load your Azure Machine Learning workspace using the `get()` function with your subscription and workspace information. Create an experiment within your workspace to store and monitor your model runs.
 
 
 ```python

articles/stream-analytics/stream-analytics-introduction.md

@@ -57,7 +57,7 @@ Azure Stream Analytics uses a simple SQL-based query language that has been augm
 
 The Stream Analytics query language offers a wide array of functions for analyzing and processing streaming data. This query language supports simple data manipulation, aggregation functions, and complex geospatial functions. You can edit queries in the portal and test them using sample data that is extracted from a live stream.
 
-You can extend the capabilities of the query language by defining and invoking additional functions. You can define function calls in the Azure Machine Learning service to take advantage of Azure Machine Learning solutions, and integrate JavaScript or C# user-defined functions (UDFs) or user-defined aggregates to perform complex calculations as part a Stream Analytics query.
+You can extend the capabilities of the query language by defining and invoking additional functions. You can define function calls in Azure Machine Learning  to take advantage of Azure Machine Learning solutions, and integrate JavaScript or C# user-defined functions (UDFs) or user-defined aggregates to perform complex calculations as part a Stream Analytics query.
 
 ## Fully managed
 

includes/aml-dsvm-server.md

@@ -11,7 +11,7 @@ ms.topic: "include"
 ms.date: 01/25/2019
 ---
 
-1. [Create an Azure Machine Learning service workspace](../articles/machine-learning/service/how-to-manage-workspace.md).
+1. [Create an Azure Machine Learning workspace](../articles/machine-learning/service/how-to-manage-workspace.md).
 
 1. Clone [the GitHub repository](https://aka.ms/aml-notebooks).
 

includes/aml-ui-prereq.md

@@ -10,7 +10,7 @@ ms.author: sgilley
 ms.date: 05/06/2019
 ---
 
-1. [Create an Azure Machine Learning service workspace](../articles/machine-learning/service/how-to-manage-workspace.md) if you don't have one.
+1. [Create an Azure Machine Learning workspace](../articles/machine-learning/service/how-to-manage-workspace.md) if you don't have one.
 
 1. Open your workspace in the [Azure portal](https://portal.azure.com/).  If you're not sure how to locate your workspace in the portal, see [how to find your workspace](../articles/machine-learning/service/how-to-manage-workspace.md#view).  
 

includes/aml-your-server.md

@@ -13,7 +13,7 @@ ms.date: 09/26/2019
 
 1. Use the instructions at [Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py)  to install the Azure Machine Learning SDK for Python
 
-1. Create an [Azure Machine Learning service workspace](../articles/machine-learning/service/how-to-manage-workspace.md).
+1. Create an [Azure Machine Learning workspace](../articles/machine-learning/service/how-to-manage-workspace.md).
 
 1. Write a  [configuration file](../articles/machine-learning/service/how-to-configure-environment.md#workspace) file (**aml_config/config.json**).
 

markdown templates/aml-templates/template-tutorial.md

@@ -37,7 +37,7 @@ In this tutorial, you use X to do Y or you learn how to:
 
 To complete this tutorial, you need:
 * An Azure subscription. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin. 
-* An Azure Machine Learning service workspace. Learn how to get create a workspace in the [Get started](quickstart-get-started.md#create-a-workspace) quickstart.
+* An Azure Machine Learning workspace. Learn how to get create a workspace in the [Get started](quickstart-get-started.md#create-a-workspace) quickstart.
 * A Docker engine installed and running locally. Docker's Community Edition is sufficient. Learn how to install Docker here: https://docs.docker.com/engine/installation/.