Merge pull request #107178 from PeterCLu/plu-studio-notebook-deprecation

Add deprecation notice for Studio (classic) Notebooks

Author: GitHubber17

articles/machine-learning/studio/ai-gallery-control-personal-data-dsr.md

@@ -16,6 +16,8 @@ ms.reviewer: jmartens, mldocs
 
 # View and delete in-product user data from Azure AI Gallery
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 You can view and delete your in-product user data from Azure AI Gallery using the interface or AI Gallery Catalog API. This article tells you how.
 
 [!INCLUDE [GDPR-related guidance](../../../includes/gdpr-dsr-and-stp-note.md)]
@@ -26,9 +28,9 @@ You can view and delete your in-product user data from Azure AI Gallery using th
 
 You can view items you published through the Azure AI Gallery website UI. Users can view both public and unlisted solutions, projects, experiments, and other published items:
 
-1.	Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
-2.	Click the profile picture in the top-right corner, and then the account name to load your profile page.
-3.	The profile page displays all items published to the gallery, including unlisted entries.
+1.    Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
+2.    Click the profile picture in the top-right corner, and then the account name to load your profile page.
+3.    The profile page displays all items published to the gallery, including unlisted entries.
 
 ## Use the AI Gallery Catalog API to view your data
 
@@ -39,9 +41,9 @@ Catalog responses are returned in JSON format.
 ### Get an author ID
 The author ID is based on the email address used when publishing to the Azure AI Gallery. It doesn't change:
 
-1.	Sign in to [Azure AI Gallery](https://gallery.azure.ai/).
-2.	Click the profile picture in the top-right corner, and then the account name to load your profile page.
-3.	The URL in the address bar displays the alphanumeric ID following `authorId=`. For example, for the URL: 
+1.    Sign in to [Azure AI Gallery](https://gallery.azure.ai/).
+2.    Click the profile picture in the top-right corner, and then the account name to load your profile page.
+3.    The URL in the address bar displays the alphanumeric ID following `authorId=`. For example, for the URL: 
     `https://gallery.azure.ai/Home/Author?authorId=99F1F5C6260295F1078187FA179FBE08B618CB62129976F09C6AF0923B02A5BA`
         
     Author ID: 
@@ -53,12 +55,12 @@ You need an access token to view unlisted entities through the Catalog API. With
 
 To get an access token, you need to inspect the `DataLabAccessToken` header of an HTTP request the browser makes to the Catalog API while logged in:
 
-1.	Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
-2.	Click the profile picture in the top-right corner, and then the account name to load your profile page.
-3.	Open the browser Developer Tools pane by pressing F12, select the Network tab, and refresh the page. 
+1.    Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
+2.    Click the profile picture in the top-right corner, and then the account name to load your profile page.
+3.    Open the browser Developer Tools pane by pressing F12, select the Network tab, and refresh the page. 
 4. Filter requests on the string *catalog* by typing into the Filter text box.
-5.	In requests to the URL `https://catalog.cortanaanalytics.com/entities`, find a GET request and select the *Headers* tab. Scroll down to the *Request Headers* section.
-6.	Under the header `DataLabAccessToken` is the alphanumeric token. To help keep your data secure, don't share this token.
+5.    In requests to the URL `https://catalog.cortanaanalytics.com/entities`, find a GET request and select the *Headers* tab. Scroll down to the *Request Headers* section.
+6.    Under the header `DataLabAccessToken` is the alphanumeric token. To help keep your data secure, don't share this token.
 
 ### View user information
 Using the author ID you got in the previous steps, view information in a user's profile by replacing `[AuthorId]` in the following URL:
@@ -90,9 +92,9 @@ For example:
 
 This query displays only public entities. To view all your entities, including unlisted ones, provide the access token obtained from the previous section.
 
-1.	Using a tool like [Postman](https://www.getpostman.com), create an HTTP GET request to the catalog URL as described in [Get your access token](#get-your-access-token).
-2.	Create an HTTP request header called `DataLabAccessToken`, with the value set to the access token.
-3.	Submit the HTTP request.
+1.    Using a tool like [Postman](https://www.getpostman.com), create an HTTP GET request to the catalog URL as described in [Get your access token](#get-your-access-token).
+2.    Create an HTTP request header called `DataLabAccessToken`, with the value set to the access token.
+3.    Submit the HTTP request.
 
 > [!TIP]
 > If unlisted entities are not showing up in responses from the Catalog API, the user may have an invalid or expired access token. Sign out of the Azure AI Gallery, and then repeat the steps in [Get your access token](#get-your-access-token) to renew the token. 

articles/machine-learning/studio/algorithm-parameters-optimize.md

@@ -14,6 +14,8 @@ ms.date: 11/29/2017
 ---
 # Choose parameters to optimize your algorithms in Azure Machine Learning Studio (classic)
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 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*.
 
 
@@ -36,7 +38,7 @@ You can define the parameter set at the model initialization step. The parameter
 
 ![Two-class boosted decision tree, single parameter](./media/algorithm-parameters-optimize/fig2.png)
 
- Alternately, you can define the maximum and minimum points of the grid and the total number of points to be generated with **Use Range Builder**. By default, the parameter values are generated on a linear scale. But if **Log Scale** is checked, the values are generated in the log scale (that is, the ratio of the adjacent points is constant instead of their difference). For integer parameters, you can define a range by using a hyphen. For example, “1-10” means that all integers between 1 and 10 (both inclusive) form the parameter set. A mixed mode is also supported. For example, the parameter set “1-10, 20, 50” would include integers 1-10, 20, and 50.
+ Alternately, you can define the maximum and minimum points of the grid and the total number of points to be generated with **Use Range Builder**. By default, the parameter values are generated on a linear scale. But if **Log Scale** is checked, the values are generated in the log scale (that is, the ratio of the adjacent points is constant instead of their difference). For integer parameters, you can define a range by using a hyphen. For example, "1-10" means that all integers between 1 and 10 (both inclusive) form the parameter set. A mixed mode is also supported. For example, the parameter set "1-10, 20, 50" would include integers 1-10, 20, and 50.
 
 ![Two-class boosted decision tree, parameter range](./media/algorithm-parameters-optimize/fig3.png)
 

articles/machine-learning/studio/azure-ml-customer-churn-scenario.md

@@ -13,6 +13,9 @@ ms.custom: seodec18
 ms.date: 12/18/2017
 ---
 # Analyze Customer Churn using Azure Machine Learning Studio (classic)
+
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 ## Overview
 This article presents a reference implementation of a customer churn analysis project that is built by using Azure Machine Learning Studio (classic). In this article, we discuss associated generic models for holistically solving the problem of industrial customer churn. We also measure the accuracy of models that are built by using Machine Learning, and assess directions for further development.  
 

articles/machine-learning/studio/azure-ml-netsharp-reference-guide.md

@@ -14,6 +14,8 @@ ms.date: 03/01/2018
 ---
 # Guide to Net# neural network specification language for Azure Machine Learning Studio (classic)
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 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:

articles/machine-learning/studio/basics-infographic-with-algorithm-examples.md

@@ -14,6 +14,8 @@ ms.date: 03/04/2019
 ---
 # Downloadable Infographic: Machine learning basics with algorithm examples
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 Download this easy-to-understand infographic overview of machine learning basics to learn about popular algorithms used to answer common machine learning questions. Algorithm examples help the machine learning beginner understand which algorithms to use and what they're used for.
 
 ## Popular algorithms in Machine Learning Studio (classic)

articles/machine-learning/studio/consume-web-services.md

@@ -14,6 +14,8 @@ ms.date: 06/02/2017
 ---
 # How to consume an Azure Machine Learning Studio (classic) web service
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 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:

articles/machine-learning/studio/consuming-from-excel.md

@@ -14,7 +14,9 @@ ms.date: 02/01/2018
 ---
 # Consuming an Azure Machine Learning Studio (classic) Web Service from Excel
 
- Azure Machine Learning Studio (classic) makes it easy to call web services directly from Excel without the need to write any code.
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
+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).
 

articles/machine-learning/studio/convert-training-experiment-to-scoring-experiment.md

@@ -13,6 +13,8 @@ ms.date: 03/28/2017
 ---
 # How to prepare your model for deployment in Azure Machine Learning Studio (classic)
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 Azure Machine Learning Studio (classic) gives you the tools you need to develop a predictive analytics model and then operationalize it by deploying it as an Azure web service.
 
 To do this, you use Studio (classic) to create an experiment - called a *training experiment* - where you train, score, and edit your model. Once you're satisfied, you get your model ready to deploy by converting your training experiment to a *predictive experiment* that's configured to score user data.

articles/machine-learning/studio/create-endpoint.md

@@ -14,6 +14,8 @@ ms.date: 02/15/2019
 ---
 # Create endpoints for deployed Azure Machine Learning Studio (classic) web services
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 > [!NOTE]
 > This topic describes techniques applicable to a **Classic** Machine Learning web service.
 

articles/machine-learning/studio/create-experiment.md

@@ -16,6 +16,8 @@ ms.date: 02/06/2019
 
 # Quickstart: Create your first data science experiment in Azure Machine Learning Studio (classic)
 
+[!INCLUDE [Notebook deprecation notice](../../../includes/aml-studio-notebook-notice.md)]
+
 [!INCLUDE [Designer notice](../../../includes/designer-notice.md)]
 
 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.
@@ -128,7 +130,7 @@ Now that the data is clean, we're ready to specify what features we're going to
 
 ## Define features
 
-In machine learning, *features* are individual measurable properties of something you’re interested in. In our dataset, each row represents one automobile, and each column is a feature of that automobile.
+In machine learning, *features* are individual measurable properties of something you're interested in. In our dataset, each row represents one automobile, and each column is a feature of that automobile.
 
 Finding a good set of features for creating a predictive model requires experimentation and knowledge about the problem you want to solve. Some features are better for predicting the target than others. Some features have a strong correlation with other features and can be removed. For example, city-mpg and highway-mpg are closely related so we can keep one and remove the other without significantly affecting the prediction.