Remove notebook notice

PeterCLu · PeterCLu · commit bbdd0b8c7065 · 2020-05-27T09:59:44.000-07:00
diff --git a/articles/machine-learning/studio/algorithm-parameters-optimize.md b/articles/machine-learning/studio/algorithm-parameters-optimize.md
@@ -14,8 +14,6 @@ ms.date: 11/29/2017
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 # Choose parameters to optimize your algorithms in Azure Machine Learning Studio (classic)
 
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 This topic describes how to choose the right hyperparameter set for an algorithm in Azure Machine Learning Studio (classic). Most machine learning algorithms have parameters to set. When you train a model, you need to provide values for those parameters. The efficacy of the trained model depends on the model parameters that you choose. The process of finding the optimal set of parameters is known as *model selection*.
 
 
diff --git a/articles/machine-learning/studio/azure-ml-netsharp-reference-guide.md b/articles/machine-learning/studio/azure-ml-netsharp-reference-guide.md
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 # Guide to Net# neural network specification language for Azure Machine Learning Studio (classic)
 
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 Net# is a language developed by Microsoft that is used to define complex neural network architectures such as deep neural networks or convolutions of arbitrary dimensions. You can use complex structures to improve learning on data such as image, video, or audio.
 
 You can use a Net# architecture specification in these contexts:
diff --git a/articles/machine-learning/studio/consume-web-services.md b/articles/machine-learning/studio/consume-web-services.md
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 # How to consume an Azure Machine Learning Studio (classic) web service
 
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 Once you deploy an Azure Machine Learning Studio (classic) predictive model as a Web service, you can use a REST API to send it data and get predictions. You can send the data in real-time or in batch mode.
 
 You can find more information about how to create and deploy a Machine Learning Web service using Machine Learning Studio (classic) here:
diff --git a/articles/machine-learning/studio/consuming-from-excel.md b/articles/machine-learning/studio/consuming-from-excel.md
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 # Consuming an Azure Machine Learning Studio (classic) Web Service from Excel
 
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 Azure Machine Learning Studio (classic) makes it easy to call web services directly from Excel without the need to write any code.
 
 If you are using Excel 2013 (or later) or Excel Online, then we recommend that you use the Excel [Excel add-in](excel-add-in-for-web-services.md).
diff --git a/articles/machine-learning/studio/create-endpoint.md b/articles/machine-learning/studio/create-endpoint.md
@@ -14,8 +14,6 @@ ms.date: 02/15/2019
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 # Create endpoints for deployed Azure Machine Learning Studio (classic) web services
 
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 > [!NOTE]
 > This topic describes techniques applicable to a **Classic** Machine Learning web service.
 
diff --git a/articles/machine-learning/studio/create-experiment.md b/articles/machine-learning/studio/create-experiment.md
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 # Quickstart: Create your first data science experiment in Azure Machine Learning Studio (classic)
 
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 In this quickstart, you create a machine learning experiment in [Azure Machine Learning Studio (classic)](what-is-ml-studio.md) that predicts the price of a car based on different variables such as make and technical specifications.
diff --git a/articles/machine-learning/studio/create-models-and-endpoints-with-powershell.md b/articles/machine-learning/studio/create-models-and-endpoints-with-powershell.md
@@ -14,8 +14,6 @@ ms.date: 04/04/2017
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 # Create multiple web service endpoints from one experiment with ML Studio (classic) and PowerShell
 
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 Here's a common machine learning problem: You want to create many models that have the same training workflow and use the same algorithm. But you want them to have different training datasets as input. This article shows you how to do this at scale in Azure Machine Learning Studio (classic) using just a single experiment.
 
 For example, let's say you own a global bike rental franchise business. You want to build a regression model to predict the rental demand based on historic data. You have 1,000 rental locations across the world and you've collected a dataset for each location. They include important features such as date, time, weather, and traffic that are specific to each location.
diff --git a/articles/machine-learning/studio/create-workspace.md b/articles/machine-learning/studio/create-workspace.md
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 # Create and share an Azure Machine Learning Studio (classic) workspace
 
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 To use Azure Machine Learning Studio (classic), you need to have a Machine Learning Studio (classic) workspace. This workspace contains the tools you need to create, manage, and publish experiments.
 
 ## Create a Studio (classic) workspace
diff --git a/articles/machine-learning/studio/deploy-a-machine-learning-web-service.md b/articles/machine-learning/studio/deploy-a-machine-learning-web-service.md
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 # Deploy an Azure Machine Learning Studio (classic) web service
 
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 Azure Machine Learning Studio (classic) enables you to build and test a predictive analytic solution. Then you can deploy the solution as a web service.
 
 Machine Learning Studio (classic) web services provide an interface between an application and a Machine Learning Studio (classic) workflow scoring model. An external application can communicate with a Machine Learning Studio (classic) workflow scoring model in real time. A call to a Machine Learning Studio (classic) web service returns prediction results to an external application. To make a call to a web service, you pass an API key that was created when you deployed the web service. A Machine Learning Studio (classic) web service is based on REST, a popular architecture choice for web programming projects.
diff --git a/articles/machine-learning/studio/deploy-consume-web-service-guide.md b/articles/machine-learning/studio/deploy-consume-web-service-guide.md
@@ -14,8 +14,6 @@ ms.date: 04/19/2017
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 # Azure Machine Learning Studio (classic) Web Services: Deployment and consumption
 
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 You can use Azure Machine Learning Studio (classic) to deploy machine learning workflows and models as web services. These web services can then be used to call the machine learning models from applications over the Internet to do predictions in real time or in batch mode. Because the web services are RESTful, you can call them from various programming languages and platforms, such as .NET and Java, and from applications, such as Excel.
 
 The next sections provide links to walkthroughs, code, and documentation to help get you started.
diff --git a/articles/machine-learning/studio/deploy-with-resource-manager-template.md b/articles/machine-learning/studio/deploy-with-resource-manager-template.md
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 # Deploy Azure Machine Learning Studio (classic) Workspace Using Azure Resource Manager
 
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 Using an Azure Resource Manager deployment template saves you time by giving you a scalable way to deploy interconnected components with a validation and retry mechanism. To set up Azure Machine Learning Studio (classic) Workspaces, for example, you need to first configure an Azure storage account and then deploy your workspace. Imagine doing this manually for hundreds of workspaces. An easier alternative is to use an Azure Resource Manager template to deploy an Studio (classic) Workspace and all its dependencies. This article takes you through this process step-by-step. For a great overview of Azure Resource Manager, see [Azure Resource Manager overview](../../azure-resource-manager/management/overview.md).
 
 [!INCLUDE [updated-for-az](../../../includes/updated-for-az.md)]
diff --git a/articles/machine-learning/studio/evaluate-model-performance.md b/articles/machine-learning/studio/evaluate-model-performance.md
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 # How to evaluate model performance in Azure Machine Learning Studio (classic)
 
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 This article demonstrates how to evaluate the performance of a model in Azure Machine Learning Studio (classic) and provides a brief explanation of the metrics available for this task. Three common supervised learning scenarios are presented: 
 
 * regression
diff --git a/articles/machine-learning/studio/excel-add-in-for-web-services.md b/articles/machine-learning/studio/excel-add-in-for-web-services.md
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 # Excel Add-in for Azure Machine Learning Studio (classic) web services
 
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 Excel makes it easy to call web services directly without the need to write any code.
 
 ## Steps to Use an Existing web service in the Workbook
diff --git a/articles/machine-learning/studio/execute-python-scripts.md b/articles/machine-learning/studio/execute-python-scripts.md
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 # Execute Python machine learning scripts in Azure Machine Learning Studio (classic)
 
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 Python is a valuable tool in the tool chest of many data scientists. It's used in every stage of typical machine learning workflows including data exploration, feature extraction, model training and validation, and deployment.
 
 This article describes how you can use the Execute Python Script module to use Python code in your Azure Machine Learning Studio (classic) experiments and web services.
diff --git a/articles/machine-learning/studio/import-data.md b/articles/machine-learning/studio/import-data.md
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 # Import your training data into Azure Machine Learning Studio (classic) from various data sources
 
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 To use your own data in Machine Learning Studio (classic) to develop and train a predictive analytics solution, you can use data from: 
 
 * **Local file** - Load local data ahead of time from your hard drive to create a dataset module in your workspace
diff --git a/articles/machine-learning/studio/interpret-model-results.md b/articles/machine-learning/studio/interpret-model-results.md
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 # Interpret model results in Azure Machine Learning Studio (classic)
 
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 This topic explains how to visualize and interpret prediction results in Azure Machine Learning Studio (classic). After you have trained a model and done predictions on top of it ("scored the model"), you need to understand and interpret the prediction result.
 
 There are four major kinds of machine learning models in Azure Machine Learning Studio (classic):
diff --git a/articles/machine-learning/studio/manage-experiment-iterations.md b/articles/machine-learning/studio/manage-experiment-iterations.md
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 # Manage experiment runs in Azure Machine Learning Studio (classic)
 
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 Developing a predictive analysis model is an iterative process - as you modify the various functions and parameters of your experiment, your results converge until you are satisfied that you have a trained, effective model. Key to this process is tracking the various iterations of your experiment parameters and configurations.
 
 You can review previous runs of your experiments at any time in order to challenge, revisit, and ultimately either confirm or refine previous assumptions. When you run an experiment, Machine Learning Studio (classic) keeps a history of the run, including dataset, module, and port connections and parameters. This history also captures results, runtime information such as start and stop times, log messages, and execution status. You can look back at any of these runs at any time to review the chronology of your experiment and intermediate results. You can even use a previous run of your experiment to launch into a new phase of inquiry and discovery on your path to creating simple, complex, or even ensemble modeling solutions.
diff --git a/articles/machine-learning/studio/manage-new-webservice.md b/articles/machine-learning/studio/manage-new-webservice.md
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 # Manage a web service using the Azure Machine Learning Studio (classic) Web Services portal
 
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 You can manage your Machine Learning New and Classic Web services using the Microsoft Azure Machine Learning Web Services portal. Since Classic Web services and New Web services are based on different underlying technologies, you have slightly different management capabilities for each of them.
 
 In the Machine Learning Web Services portal you can:
diff --git a/articles/machine-learning/studio/manage-web-service-endpoints-using-api-management.md b/articles/machine-learning/studio/manage-web-service-endpoints-using-api-management.md
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 # Manage Azure Machine Learning Studio (classic) web services using API Management
 
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 ## Overview
 This guide shows you how to quickly get started using API Management to manage your Azure Machine Learning Studio (classic) web services.
 
diff --git a/articles/machine-learning/studio/manage-workspace.md b/articles/machine-learning/studio/manage-workspace.md
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 # Manage an Azure Machine Learning Studio (classic) workspace
 
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 > [!NOTE]
 > For information on managing Web services in the Machine Learning Web Services portal, see [Manage a Web service using the Azure Machine Learning Web Services portal](manage-new-webservice.md).
 > 
diff --git a/articles/machine-learning/studio/model-progression-experiment-to-web-service.md b/articles/machine-learning/studio/model-progression-experiment-to-web-service.md
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 # How a Machine Learning Studio (classic) model progresses from an experiment to a Web service
 
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 Azure Machine Learning Studio (classic) provides an interactive canvas that allows you to develop, run, test, and iterate an ***experiment*** representing a predictive analysis model. There are a wide variety of modules available that can:
 
 * Input data into your experiment
diff --git a/articles/machine-learning/studio/powershell-module.md b/articles/machine-learning/studio/powershell-module.md
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 # PowerShell modules for Azure Machine Learning Studio (classic)
 
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 Using PowerShell modules, you can programmatically manage your Studio (classic) resources and assets such as workspaces, datasets, and web services.
 
-You can interact with Studio (classic) resources using three Powershell modules:
+You can interact with Studio (classic) resources using three PowerShell modules:
 
 * [Azure PowerShell Az](#az-rm) released in 2018, includes all functionality of AzureRM, although with different cmdlet names
 * [AzureRM](#az-rm) released in 2016, replaced by PowerShell Az
diff --git a/articles/machine-learning/studio/r-get-started.md b/articles/machine-learning/studio/r-get-started.md
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 # Get started with Azure Machine Learning Studio (classic) in R
 
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 <!-- Stephen F Elston, Ph.D. -->
 In this tutorial, you learn how to use ML Studio (classic) to create, test and execute R code. In the end, you will have a complete forecasting solution.  
 
diff --git a/articles/machine-learning/studio/retrain-classic-web-service.md b/articles/machine-learning/studio/retrain-classic-web-service.md
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 # Retrain and deploy a classic Studio (classic) web service
 
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 Retraining machine learning models is one way to ensure they stay accurate and based on the most relevant data available. This article will show you how to retrain a classic Studio (classic) web service. For a guide on how to retrain a new Studio (classic) web service, [view this how-to article.](retrain-machine-learning-model.md)
 
 ## Prerequisites
diff --git a/articles/machine-learning/studio/retrain-machine-learning-model.md b/articles/machine-learning/studio/retrain-machine-learning-model.md
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 # Retrain and deploy a machine learning model
 
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 Retraining is one way to ensure machine learning models stay accurate and based on the most relevant data available. This article shows how to retrain and deploy a machine learning model as a new web service in Studio (classic). If you're looking to retrain a classic web service, [view this how-to article.](retrain-classic-web-service.md)
 
 This article assumes you already have a predictive web service deployed. If you don't already have a predictive web service, [learn how to deploy a Studio (classic) web service here.](deploy-a-machine-learning-web-service.md)
diff --git a/articles/machine-learning/studio/tutorial-part1-credit-risk.md b/articles/machine-learning/studio/tutorial-part1-credit-risk.md
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 # Tutorial 1: Predict credit risk - Azure Machine Learning Studio (classic)
 
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 In this tutorial, you take an extended look at the process of developing a predictive analytics solution. You develop a simple model in Machine Learning Studio (classic).  You then deploy the model as an Azure Machine Learning web service.  This deployed model can make predictions using new data. This tutorial is **part one of a three-part tutorial series**.
diff --git a/articles/machine-learning/studio/tutorial-part2-credit-risk-train.md b/articles/machine-learning/studio/tutorial-part2-credit-risk-train.md
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 # Tutorial 2: Train credit risk models - Azure Machine Learning Studio (classic)
 
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 In this tutorial, you take an extended look at the process of developing a predictive analytics solution. You develop a simple model in Machine Learning Studio (classic).  You then deploy the model as an Azure Machine Learning web service.  This deployed model can make predictions using new data. This tutorial is **part two of a three-part tutorial series**.
 
 Suppose you need to predict an individual's credit risk based on the information they gave on a credit application.  
diff --git a/articles/machine-learning/studio/tutorial-part3-credit-risk-deploy.md b/articles/machine-learning/studio/tutorial-part3-credit-risk-deploy.md
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 # Tutorial 3: Deploy credit risk model - Azure Machine Learning Studio (classic)
 
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 In this tutorial, you take an extended look at the process of developing a predictive analytics solution. You develop a simple model in Machine Learning Studio (classic).  You then deploy the model as an Azure Machine Learning web service.  This deployed model can make predictions using new data. This tutorial is **part three of a three-part tutorial series**.
 
 Suppose you need to predict an individual's credit risk based on the information they gave on a credit application.  
diff --git a/articles/machine-learning/studio/use-data-from-an-on-premises-sql-server.md b/articles/machine-learning/studio/use-data-from-an-on-premises-sql-server.md
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 # Perform analytics with Azure Machine Learning Studio (classic) using an on-premises SQL Server database
 
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 Often enterprises that work with on-premises data would like to take advantage of the scale and agility of the cloud for their machine learning workloads. But they don't want to disrupt their current business processes and workflows by moving their on-premises data to the cloud. Azure Machine Learning Studio (classic) now supports reading your data from an on-premises SQL Server database and then training and scoring a model with this data. You no longer have to manually copy and sync the data between the cloud and your on-premises server. Instead, the **Import Data** module in Azure Machine Learning Studio (classic) can now read directly from your on-premises SQL Server database for your training and scoring jobs.
 
 This article provides an overview of how to ingress on-premises SQL server data into Azure Machine Learning Studio (classic). It assumes that you're familiar with Studio (classic) concepts like workspaces, modules, datasets, experiments, *etc.*.
diff --git a/articles/machine-learning/studio/use-sample-datasets.md b/articles/machine-learning/studio/use-sample-datasets.md
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 # Use the sample datasets in Azure Machine Learning Studio (classic)
 
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 [top]: #machine-learning-sample-datasets
 
 When you create a new workspace in Azure Machine Learning Studio (classic), a number of sample datasets and experiments are included by default. Many of these sample datasets are used by the sample models in the [Azure AI Gallery](https://gallery.azure.ai/). Others are included as examples of various types of data typically used in machine learning.
diff --git a/articles/machine-learning/studio/version-control.md b/articles/machine-learning/studio/version-control.md
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 # Application Lifecycle Management in Azure Machine Learning Studio (classic)
 
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 Azure Machine Learning Studio (classic) is a tool for developing machine learning experiments that are operationalized in the Azure cloud platform. It is like the Visual Studio IDE and scalable cloud service merged into a single platform. You can incorporate standard Application Lifecycle Management (ALM) practices from versioning various assets to automated execution and deployment, into Azure Machine Learning Studio (classic). This article discusses some of the options and approaches.
 
 [!INCLUDE [updated-for-az](../../../includes/updated-for-az.md)]
diff --git a/articles/machine-learning/studio/web-service-error-codes.md b/articles/machine-learning/studio/web-service-error-codes.md
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 # Azure Machine Learning Studio (classic) REST API Error Codes
 
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 The following error codes could be returned by an operation on an Azure Machine Learning Studio (classic) web service.
  
 ## BadArgument (HTTP status code 400)
diff --git a/articles/machine-learning/studio/web-service-parameters.md b/articles/machine-learning/studio/web-service-parameters.md
diff --git a/articles/machine-learning/studio/web-services-logging.md b/articles/machine-learning/studio/web-services-logging.md
diff --git a/articles/machine-learning/studio/web-services-that-use-import-export-modules.md b/articles/machine-learning/studio/web-services-that-use-import-export-modules.md
