rebranding some more...

articles/azure-subscription-service-limits.md

@@ -51,7 +51,7 @@ In the following list of limits, a new table reflects any differences in limits
 * [Azure Firewall](#azure-firewall-limits)
 * [Azure Functions](#functions-limits)
 * [Azure Kubernetes Service](#azure-kubernetes-service-limits)
-* [Azure Machine Learning Service](#azure-machine-learning-service-limits)
+* [Azure Machine Learning](#azure-machine-learning-service-limits)
 * [Azure Maps](#azure-maps-limits)
 * [Azure Monitor](#azure-monitor-limits)
 * [Azure Policy](#azure-policy-limits)
@@ -149,7 +149,7 @@ The following table details the features and limits of the Basic, Standard, and
 ### Azure Kubernetes Service limits
 [!INCLUDE [container-service-limits](../includes/container-service-limits.md)]
 
-### Azure Machine Learning Service limits
+### Azure Machine Learning limits
 The latest values for Azure Machine Learning Compute quotas can be found in the [Azure Machine Learning quota page](../articles/machine-learning/service/how-to-manage-quotas.md)
 
 ### Networking limits

articles/cdn/cdn-billing.md

@@ -109,7 +109,7 @@ If you use one of the following Azure services as your CDN origin, you will not
 - HDInsight
 - Azure Cosmos DB
 - Azure Data Lake Store
-- Azure Machine Learning service 
+- Azure Machine Learning 
 - Azure SQL database
 - Azure Cache for Redis
 

articles/data-factory/compute-linked-services.md

@@ -22,7 +22,7 @@ The following table provides a list of compute environments supported by Data Fa
 | [On-demand HDInsight cluster](#azure-hdinsight-on-demand-linked-service) or [your own HDInsight cluster](#azure-hdinsight-linked-service) | [Hive](transform-data-using-hadoop-hive.md), [Pig](transform-data-using-hadoop-pig.md), [Spark](transform-data-using-spark.md), [MapReduce](transform-data-using-hadoop-map-reduce.md), [Hadoop Streaming](transform-data-using-hadoop-streaming.md) |
 | [Azure Batch](#azure-batch-linked-service)                   | [Custom](transform-data-using-dotnet-custom-activity.md)     |
 | [Azure Machine Learning Studio](#azure-machine-learning-studio-linked-service) | [Machine Learning activities: Batch Execution and Update Resource](transform-data-using-machine-learning.md) |
-| [Azure Machine Learning Service](#azure-machine-learning-service-linked-service) | [Azure Machine Learning Execute Pipeline](transform-data-machine-learning-service.md) |
+| [Azure Machine Learning](#azure-machine-learning-service-linked-service) | [Azure Machine Learning Execute Pipeline](transform-data-machine-learning-service.md) |
 | [Azure Data Lake Analytics](#azure-data-lake-analytics-linked-service) | [Data Lake Analytics U-SQL](transform-data-using-data-lake-analytics.md) |
 | [Azure SQL](#azure-sql-database-linked-service), [Azure SQL Data Warehouse](#azure-sql-data-warehouse-linked-service), [SQL Server](#sql-server-linked-service) | [Stored Procedure](transform-data-using-stored-procedure.md) |
 | [Azure Databricks](#azure-databricks-linked-service)         | [Notebook](transform-data-databricks-notebook.md), [Jar](transform-data-databricks-jar.md), [Python](transform-data-databricks-python.md) |

articles/data-factory/transform-data-machine-learning-service.md

@@ -1,6 +1,6 @@
 ---
-title: Execute Azure Machine Learning service pipelines in your Azure Data Factory pipelines | Microsoft Docs
-description: Learn how to run your Azure Machine Learning service pipelines in your Azure Data Factory pipelines. 
+title: Execute Azure Machine Learning pipelines in your Azure Data Factory pipelines | Microsoft Docs
+description: Learn how to run your Azure Machine Learning pipelines in your Azure Data Factory pipelines. 
 services: data-factory
 documentationcenter: ''
 ms.service: data-factory
@@ -11,9 +11,9 @@ ms.date: 10/10/2019
 author: djpmsft
 ms.author: daperlov
 ---
-# Execute Azure Machine Learning service pipelines in Azure Data Factory
+# Execute Azure Machine Learning pipelines in Azure Data Factory
 
-Run your Azure Machine Learning service pipelines as a step in your Azure Data Factory pipelines. The Machine Learning Execute Pipeline activity enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.
+Run your Azure Machine Learning pipelines as a step in your Azure Data Factory pipelines. The Machine Learning Execute Pipeline activity enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.
 
 ## Syntax
 
@@ -42,11 +42,11 @@ Property | Description | Allowed values | Required
 -------- | ----------- | -------------- | --------
 name | Name of the activity in the pipeline | String | Yes
 type | Type of activity is ‘AzureMLExecutePipeline’ | String | Yes
-linkedServiceName | Linked Service to Azure Machine Learning Service | Linked service reference | Yes
+linkedServiceName | Linked Service to Azure Machine Learning | Linked service reference | Yes
 mlPipelineId | ID of the published Azure Machine Learning pipeline | String (or expression with resultType of string) | Yes
 experimentName | Run history experiment name of the Machine Learning pipeline run | String (or expression with resultType of string) | No
 mlPipelineParameters | Key, Value pairs to be passed to the published Azure Machine Learning pipeline endpoint. Keys must match the names of pipeline parameters defined in the published Machine Learning pipeline | Object with key value pairs (or Expression with resultType object) | No
-mlParentRunId | The parent Azure Machine Learning Service pipeline run ID | String (or expression with resultType of string) | No
+mlParentRunId | The parent Azure Machine Learning pipeline run ID | String (or expression with resultType of string) | No
 continueOnStepFailure | Whether to continue execution of other steps in the Machine Learning pipeline run if a step fails | boolean | No
 
 ## Next steps

articles/hdinsight/spark/apache-spark-run-machine-learning-automl.md

@@ -70,5 +70,5 @@ In the [automated machine learning configuration](https://docs.microsoft.com/pyt
 ## Next steps
 
 * For more information on the motivation behind automated machine learning, see [Release models at pace using Microsoft’s automated machine learning!](https://azure.microsoft.com/blog/release-models-at-pace-using-microsoft-s-automl/)
-* For more details on using Azure ML Automated ML capabilities, see [New automated machine learning capabilities in Azure Machine Learning service](https://azure.microsoft.com/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/)
+* For more details on using Azure ML Automated ML capabilities, see [New automated machine learning capabilities in Azure Machine Learning](https://azure.microsoft.com/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/)
 * [AutoML project from Microsoft Research](https://www.microsoft.com/research/project/automl/)

articles/iot-edge/tutorial-deploy-machine-learning.md

@@ -15,7 +15,7 @@ ms.custom: "mvc, seodec18"
 
 Use Azure Notebooks to develop a machine learning module and deploy it to a Linux device running Azure IoT Edge. 
 
-You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning service on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/service/how-to-deploy-to-iot.md).
+You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/service/how-to-deploy-to-iot.md).
 
 The Azure Machine Learning module that you create in this tutorial reads the environmental data generated by your device and labels the messages as anomalous or not.
 
@@ -50,7 +50,7 @@ Cloud resources:
 
 ## Create and deploy Azure Machine Learning module
 
-In this section, you convert trained machine learning model files and into an Azure Machine Learning service container. All the components required for the Docker image are in the [AI Toolkit for Azure IoT Edge Git repo](https://github.com/Azure/ai-toolkit-iot-edge/tree/master/IoT%20Edge%20anomaly%20detection%20tutorial). Follow these steps to upload that repository into Microsoft Azure Notebooks to create the container and push it to Azure Container Registry.
+In this section, you convert trained machine learning model files and into an Azure Machine Learning container. All the components required for the Docker image are in the [AI Toolkit for Azure IoT Edge Git repo](https://github.com/Azure/ai-toolkit-iot-edge/tree/master/IoT%20Edge%20anomaly%20detection%20tutorial). Follow these steps to upload that repository into Microsoft Azure Notebooks to create the container and push it to Azure Container Registry.
 
 
 1. Navigate to your Azure Notebooks projects. You can get there from your Azure Machine Learning workspace in the [Azure portal](https://portal.azure.com) or by signing in to [Microsoft Azure Notebooks](https://notebooks.azure.com/home/projects) with your Azure account.
@@ -144,7 +144,7 @@ The following steps show you how to set up Visual Studio Code to monitor device-
 
 5. Observe the messages coming from tempSensor every five seconds. The message body contains a property called **anomaly**, which the machinelearningmodule provides with a true or false value. The **AzureMLResponse** property contains the value "OK" if the model ran successfully.
 
-   ![Azure Machine Learning service response in message body](./media/tutorial-deploy-machine-learning/ml-output.png)
+   ![Azure Machine Learning response in message body](./media/tutorial-deploy-machine-learning/ml-output.png)
 
 ## Clean up resources
 

articles/marketplace/cloud-partner-portal/consulting-services/cpp-consulting-service-define-offer-settings.md

@@ -54,7 +54,7 @@ The following list provides several well-named offer names:
 -   Essentials for Professional Services: 1-Hr Briefing
 -   Cloud Migration Platform: 1-Hr Briefing
 -   PowerApps and Microsoft Flow: 1-Day Workshop
--   Azure Machine Learning Services: 3-Wk PoC
+-   Azure Machine Learnings: 3-Wk PoC
 -   Brick and Click Retail Solution: 1-Hr Briefing
 -   Bring Your Own Data: 1-Wk Workshop
 -   Cloud Analytics: 3-Day Workshop

articles/notebooks/azure-notebooks-overview.md

@@ -113,4 +113,4 @@ To discuss your questions about Azure Notebooks, file an issue on the [GitHub re
   - [Present a slide show](present-jupyter-notebooks-slideshow.md)
   - [Work with data files](work-with-project-data-files.md)
   - [Access data resources](access-data-resources-jupyter-notebooks.md)
-  - [Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md)
+  - [Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md)

articles/notebooks/azure-notebooks-samples.md

@@ -25,7 +25,7 @@ Once you've identified a notebook you'd like to explore, here are a few details
 
 ## Great sample notebooks
 
-- [Azure Notebooks starter set](https://notebooks.azure.com/#sample-redirect) includes introductions to Python, R, and F#, along with notebooks demonstrating data access, Azure Machine Learning Services, and a few data science exercises.
+- [Azure Notebooks starter set](https://notebooks.azure.com/#sample-redirect) includes introductions to Python, R, and F#, along with notebooks demonstrating data access, Azure Machine Learning, and a few data science exercises.
 - [Introduction to Jupyter notebooks](https://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb) (jupyter.org)
 - [Introduction to Data Science](https://github.com/jakevdp/PythonDataScienceHandbook/tree/master/notebooks) by Jake Vanderplas.
 - [Gallery of interesting notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) (Jupyter project on GitHub)
@@ -38,4 +38,4 @@ Once you've identified a notebook you'd like to explore, here are a few details
 - [How to: Configure and manage projects](configure-manage-azure-notebooks-projects.md)
 - [How to: Work with data files](work-with-project-data-files.md)
 - [How to: Access data resources](access-data-resources-jupyter-notebooks.md)
-- [How to: Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md)
+- [How to: Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md)

articles/notebooks/create-clone-jupyter-notebooks.md

@@ -120,4 +120,4 @@ To clone a project:
 - [How to: Present a slide show](present-jupyter-notebooks-slideshow.md)
 - [How to: Work with data files](work-with-project-data-files.md)
 - [How to: Access data resources](access-data-resources-jupyter-notebooks.md)
-- [How to: Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md)
+- [How to: Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md)

articles/notebooks/toc.yml

@@ -46,5 +46,5 @@
     href: access-data-resources-jupyter-notebooks.md
   - name: Use Data Science Virtual Machines
     href: use-data-science-virtual-machine.md
-  - name: Use Azure Machine Learning Services
+  - name: Use Azure Machine Learning
     href: use-machine-learning-services-jupyter-notebooks.md

articles/notebooks/tutorial-create-run-jupyter-notebook.md

@@ -425,4 +425,4 @@ How-to articles:
 - [Present a slide show](present-jupyter-notebooks-slideshow.md)
 - [Work with data files](work-with-project-data-files.md)
 - [Access data resources](access-data-resources-jupyter-notebooks.md)
-- [Use Azure Machine Learning Services](use-machine-learning-services-jupyter-notebooks.md)
+- [Use Azure Machine Learning](use-machine-learning-services-jupyter-notebooks.md)

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

@@ -1,6 +1,6 @@
 ---
-title: Use Azure Machine Learning Services in Azure Notebooks
-description: An overview of the sample notebooks for Azure Machine Learnings that you can use with Azure Notebooks.
+title: Use Azure Machine Learning in Azure Notebooks
+description: An overview of the sample notebooks for Azure Machine Learning that you can use with Azure Notebooks.
 services: app-service
 documentationcenter: ''
 author: kraigb
@@ -14,16 +14,16 @@ ms.date: 12/04/2018
 ms.author: kraigb
 ---
 
-# Use Azure Machine Learning service in a notebook
+# Use Azure Machine Learning in a notebook
 
-Azure Notebooks comes pre-configured with the necessary environment to work with [Azure Machine Learning service](/azure/machine-learning/service/). You can easily clone a sample project into your Notebooks account to explore a variety of Machine Learning scenarios.
+Azure Notebooks comes pre-configured with the necessary environment to work with [Azure Machine Learning](/azure/machine-learning/service/). You can easily clone a sample project into your Notebooks account to explore a variety of Machine Learning scenarios.
 
 ## Clone the sample into your account
 
 1. Sign into [Azure Notebooks](https://notebooks.azure.com/).
 1. Select **My Projects** to go to the projects dashboard.
 1. Select the **Upload GitHub Repo** (the up arrow) button to open the **Upload GitHub Repository** popup.
-1. In the popup, enter `Azure/MachineLearningNotebooks` in **GitHub repository**, provide a name for the project in **Project Name** like "Azure Machine Learning service", provide an identifier in **Project ID**, clear **Public** if you want, then select **Import**.
+1. In the popup, enter `Azure/MachineLearningNotebooks` in **GitHub repository**, provide a name for the project in **Project Name** like "Azure Machine Learning", provide an identifier in **Project ID**, clear **Public** if you want, then select **Import**.
 
     ![Import Azure Machine Learning Notebook sample into your Notebooks account](media/azureml-import-project.png)
 
@@ -44,6 +44,6 @@ The Azure Machine Learning documentation contains a variety of other resources t
 - [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](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml)
 - [Tutorial #2: Deploy an image classification model in Azure Container Instance (ACI)](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml)
-- [Tutorial: Train a classification model with automated machine learning in Azure Machine Learning service](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models)
+- [Tutorial: Train a classification model with automated machine learning in Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models)
 
 Also see the documentation for the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py).

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

@@ -1,7 +1,7 @@
 ---
 title: 'Tutorial: Enrich an automated machine learning model'
 titleSuffix: Azure Open Datasets
-description: Learn how to leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning service to create a regression model to predict NYC taxi fare prices.
+description: Learn how to leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning to create a regression model to predict NYC taxi fare prices.
 services: open-datasets
 ms.service: open-datasets
 ms.topic: tutorial
@@ -13,7 +13,7 @@ ms.date: 05/02/2019
 
 # Tutorial: Build a regression model with automated machine learning and Open Datasets
 
-In this tutorial, you leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning service to create a regression model to predict NYC taxi fare prices. Easily download publicly available taxi, holiday and weather data, and configure an automated machine learning experiment using Azure Machine Learning service. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.
+In this tutorial, you leverage the convenience of Azure Open Datasets along with the power of Azure Machine Learning to create a regression model to predict NYC taxi fare prices. Easily download publicly available taxi, holiday and weather data, and configure an automated machine learning experiment using Azure Machine Learning. This process accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.
 
 In this tutorial you learn the following tasks:
 
@@ -1969,4 +1969,4 @@ If you don't plan to use the resources you created, delete them, so you don't in
 ## Next steps
 
 * See the Azure Open Datasets [notebooks](https://github.com/Azure/OpenDatasetsNotebooks) for more code examples.
-* Follow the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) for more information on automated machine learning in Azure Machine Learning service.
+* Follow the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) for more information on automated machine learning in Azure Machine Learning.