Merge pull request #88549 from j-martens/master

branding for Azure Machine Learning without 'service'

Author: ShannonLeavitt

articles/machine-learning/service/how-to-deploy-inferencing-gpus.md

@@ -1,7 +1,7 @@
 ---
 title: Deploy a model for inference with GPU 
-titleSuffix: Azure Machine Learning service
-description: This article teaches you how to use the Azure Machine Learning service to deploy a GPU-enabled Tensorflow deep learning model as a web service.service and score inference requests.
+titleSuffix: Azure Machine Learning
+description: This article teaches you how to use Azure Machine Learning to deploy a GPU-enabled Tensorflow deep learning model as a web service.service and score inference requests.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -14,7 +14,7 @@ ms.date: 07/24/2019
 
 # Deploy a deep learning model for inference with GPU
 
-This article teaches you how to use the Azure Machine Learning service to deploy a GPU-enabled model as a web service. The information in this article is based on deploying a model on Azure Kubernetes Service (AKS). The AKS cluster provides a GPU resource that is used by the model for inference.
+This article teaches you how to use Azure Machine Learning to deploy a GPU-enabled model as a web service. The information in this article is based on deploying a model on Azure Kubernetes Service (AKS). The AKS cluster provides a GPU resource that is used by the model for inference.
 
 Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
 
@@ -29,7 +29,7 @@ Inference, or model scoring, is the phase where the deployed model is used to ma
 
 ## Prerequisites
 
-* An Azure Machine Learning service workspace. For more information, see [Create an Azure Machine Learning service workspace](how-to-manage-workspace.md).
+* An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
 
 * A Python development environment with the Azure Machine Learning SDK installed. For more information, see [Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py).  
 
@@ -46,7 +46,7 @@ Inference, or model scoring, is the phase where the deployed model is used to ma
 To connect to an existing workspace, use the following code:
 
 > [!IMPORTANT]
-> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information on creating a workspace, see [Create and manage Azure Machine Learning service workspaces](how-to-manage-workspace.md).   For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment.md#workspace).
+> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information on creating a workspace, see [Create and manage Azure Machine Learning workspaces](how-to-manage-workspace.md).   For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment.md#workspace).
 
 ```python
 from azureml.core import Workspace
@@ -88,7 +88,7 @@ except ComputeTargetException:
 > [!IMPORTANT]
 > Azure will bill you as long as the AKS cluster exists. Make sure to delete your AKS cluster when you're done with it.
 
-For more information on using AKS with Azure Machine Learning service, see [How to deploy to Azure Kubernetes Service](how-to-deploy-azure-kubernetes-service.md).
+For more information on using AKS with Azure Machine Learning, see [How to deploy to Azure Kubernetes Service](how-to-deploy-azure-kubernetes-service.md).
 
 ## Write the entry script
 

articles/machine-learning/service/how-to-deploy-local-container-notebook-vm.md

@@ -1,7 +1,7 @@
 ---
 title: How to deploy models to Notebook VMs
-titleSuffix: Azure Machine Learning service
-description: 'Learn how to deploy your Azure Machine Learning service models as a web service using Notebook VMs.'
+titleSuffix: Azure Machine Learning
+description: 'Learn how to deploy your Azure Machine Learning models as a web service using Notebook VMs.'
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -14,17 +14,17 @@ ms.date: 08/08/2019
 
 # Deploy a model to Notebook VMs
 
-Learn how to use the Azure Machine Learning service to deploy a model as a web service on your Notebook VM. Use Notebook VMs if one of the following conditions is true:
+Learn how to use Azure Machine Learning to deploy a model as a web service on your Notebook VM. Use Notebook VMs if one of the following conditions is true:
 
 - You need to quickly deploy and validate your model.
 - You are testing a model that is under development.
 
 > [!TIP]
-> Deploying a model from a Jupyter Notebook on a notebook VM, to a web service on the same VM is a _local deployment_. In this case, the 'local' computer is the notebook VM. For more information on deployments, see [Deploy models with Azure Machine Learning service](how-to-deploy-and-where.md).
+> Deploying a model from a Jupyter Notebook on a notebook VM, to a web service on the same VM is a _local deployment_. In this case, the 'local' computer is the notebook VM. For more information on deployments, see [Deploy models with Azure Machine Learning](how-to-deploy-and-where.md).
 
 ## Prerequisites
 
-- An Azure Machine Learning service workspace with a notebook VM running. For more information, see [Setup environment and workspace](tutorial-1st-experiment-sdk-setup.md).
+- An Azure Machine Learning workspace with a notebook VM running. For more information, see [Setup environment and workspace](tutorial-1st-experiment-sdk-setup.md).
 
 ## Deploy to the notebook VMs
 

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

@@ -1,7 +1,7 @@
 ---
 title: Set up Azure Application Insights to monitor ML models
-titleSuffix: Azure Machine Learning service
-description: Monitor web services deployed with Azure Machine Learning service using Azure Application Insights
+titleSuffix: Azure Machine Learning
+description: Monitor web services deployed with Azure Machine Learning using Azure Application Insights
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -14,7 +14,7 @@ ms.custom: seoapril2019
 ---
 # Monitor your Azure Machine Learning models with Application Insights
 
-In this article, you learn how to set up Azure Application Insights for your Azure Machine Learning service. Application Insights gives you the opportunity to monitor:
+In this article, you learn how to set up Azure Application Insights for Azure Machine Learning. Application Insights gives you the opportunity to monitor:
 * Request rates, response times, and failure rates.
 * Dependency rates, response times, and failure rates.
 * Exceptions.
@@ -24,7 +24,7 @@ In this article, you learn how to set up Azure Application Insights for your Azu
 
 ## 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 service](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.
@@ -105,7 +105,7 @@ You can enable and disable Application Insights in the Azure portal.
 
 
 ## Evaluate data
-Your service's data is stored in your Application Insights account, within the same resource group as your Azure Machine Learning service.
+Your service's data is stored in your Application Insights account, within the same resource group as Azure Machine Learning.
 To view it:
 1. Go to your Machine Learning service workspace in the [Azure portal](https://portal.azure.com) and click on Application Insights link.
 

articles/machine-learning/service/how-to-enable-data-collection.md

@@ -1,6 +1,6 @@
 ---
 title: Collect data on your production models 
-titleSuffix: Azure Machine Learning service
+titleSuffix: Azure Machine Learning
 description: Learn how to collect Azure Machine Learning input model data in an Azure Blob storage.
 services: machine-learning
 ms.service: machine-learning
@@ -15,7 +15,7 @@ ms.custom: seodec18
 ---
 # Collect data for models in production
 
-In this article, you can learn how to collect input model data from the Azure Machine Learning services you've deployed into Azure Kubernetes Cluster (AKS) into an Azure Blob storage. 
+In this article, you can learn how to collect input model data from Azure Machine Learning you've deployed into Azure Kubernetes Cluster (AKS) into an Azure Blob storage. 
 
 Once enabled, this data you collect helps you:
 * [Monitor data drifts](how-to-monitor-data-drift.md) as production data enters your model
@@ -46,9 +46,9 @@ The path to the output data in the blob follows this syntax:
 
 ## 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 service](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 service workspace, a local directory containing your scripts, and the Azure Machine Learning SDK for Python installed. Learn how to get these prerequisites using the [How to configure a development environment](how-to-configure-environment.md) document.
+- An Azure Machine Learning workspace, a local directory containing your scripts, and the Azure Machine Learning SDK for Python installed. Learn how to get these prerequisites using the [How to configure a development environment](how-to-configure-environment.md) document.
 
 - A trained machine learning model to be deployed to Azure Kubernetes Service (AKS). If you don't have one, see the [train image classification model](tutorial-train-models-with-aml.md) tutorial.
 
@@ -57,7 +57,7 @@ The path to the output data in the blob follows this syntax:
 - [Set up your environment](how-to-configure-environment.md) and install the [Monitoring SDK](https://aka.ms/aml-monitoring-sdk).
 
 ## Enable data collection
-Data collection can be enabled regardless of the model being deployed through Azure Machine Learning Service or other tools. 
+Data collection can be enabled regardless of the model being deployed through Azure Machine Learning or other tools. 
 
 To enable it, you need to:
 

articles/machine-learning/service/how-to-enable-logging.md

@@ -1,7 +1,7 @@
 ---
-title: Enable logging in Azure Machine Learning service
-titleSuffix: Azure Machine Learning service
-description: Learn how to enable logging in Azure Machine Learning service using both the default Python logging package, as well as using SDK-specific functionality.
+title: Enable logging in Azure Machine Learning
+titleSuffix: Azure Machine Learning
+description: Learn how to enable logging in Azure Machine Learning using both the default Python logging package, as well as using SDK-specific functionality.
 ms.author: trbye
 author: trevorbye
 services: machine-learning
@@ -12,7 +12,7 @@ ms.reviewer: trbye
 ms.date: 07/12/2019
 ---
 
-# Enable logging in Azure Machine Learning service
+# Enable logging in Azure Machine Learning
 
 The Azure Machine Learning Python SDK allows you to enable logging using both the default Python logging package, as well as using SDK-specific functionality both for local logging and logging to your workspace in the portal. Logs provide developers with real-time information about the application state, and can help with diagnosing errors or warnings. In this article, you learn different ways of enabling logging in the following areas:
 
@@ -22,7 +22,7 @@ The Azure Machine Learning Python SDK allows you to enable logging using both th
 > * Deployed models
 > * Python `logging` settings
 
-[Create an Azure Machine Learning service workspace](how-to-manage-workspace.md). Use the [guide](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py) for more information the SDK.
+[Create an Azure Machine Learning workspace](how-to-manage-workspace.md). Use the [guide](https://docs.microsoft.com/python/api/overview/azure/ml/install?view=azure-ml-py) for more information the SDK.
 
 ## Training models and compute target logging