Merge pull request #99586 from craigcaseyMSFT/vcraic1219

User Story 1624627 - Rebranding for Azure Machine Learning

Author: GitHubber17

articles/machine-learning/compare-azure-ml-to-studio-classic.md

@@ -1,6 +1,6 @@
 ---
-title: Azure Machine Learning vs. ML Studio (classic)  
-description: How Azure Machine Learning is different from ML Studio (classic)
+title: Azure Machine Learning vs. Machine Learning Studio (classic)  
+description: How Azure Machine Learning is different from Machine Learning Studio (classic)
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -11,12 +11,12 @@ ms.date: 10/29/2019
 ---
 
 
-# How Azure Machine Learning differs from ML Studio (classic)
+# How Azure Machine Learning differs from Machine Learning Studio (classic)
 
-This article compares the features, capabilities, and interface of Azure Machine Learning to ML Studio (classic). 
+This article compares the features, capabilities, and interface of Azure Machine Learning to Machine Learning Studio (classic). 
 
-## About ML Studio (classic)
-[ML Studio (classic)](studio/what-is-ml-studio.md) is a collaborative, drag-and-drop visual workspace where you can build, test, and deploy machine learning solutions without needing to write code. It uses prebuilt and preconfigured machine learning algorithms and data-handling modules as well as a proprietary compute platform.
+## About Machine Learning Studio (classic)
+[Machine Learning Studio (classic)](studio/what-is-ml-studio.md) is a collaborative, drag-and-drop visual workspace where you can build, test, and deploy machine learning solutions without needing to write code. It uses prebuilt and preconfigured machine learning algorithms and data-handling modules as well as a proprietary compute platform.
 
 ## About Azure Machine Learning
 
@@ -27,7 +27,7 @@ Azure Machine Learning designer provides a similar drag-and-drop experience to S
 > [!TIP]
 > Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try [Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/service/ui-concept-visual-interface) (preview), which provides drag and drop ML modules __plus__ scalability, version control, and enterprise security.
 
-## Comparison: Azure Machine Learning vs. ML Studio (classic)
+## Comparison: Azure Machine Learning vs. Machine Learning Studio (classic)
 
 Here is a quick comparison.
 

articles/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough.md

@@ -171,17 +171,17 @@ Let's also try a random forest model. Random forests train a multitude of decisi
 
 ## Deploy a model to Azure Machine Learning Studio (classic)
 
-[Azure Machine Learning Studio (classic)](https://studio.azureml.net/) is a cloud service that makes it easy to build and deploy predictive analytics models. A nice feature of the classic version of Azure Machine Learning Studio is its ability to publish any R function as a web service. The Azure Machine Learning Studio R package makes deployment easy, right from your R session on the DSVM.
+[Azure Machine Learning Studio (classic)](https://studio.azureml.net/) is a cloud service that makes it easy to build and deploy predictive analytics models. A nice feature of Azure Machine Learning Studio (classic) is its ability to publish any R function as a web service. The Azure Machine Learning Studio (classic) R package makes deployment easy, right from your R session on the DSVM.
 
 To deploy the decision tree code from the preceding section, sign in to Azure Machine Learning Studio (classic). You need your workspace ID and an authorization token to sign in. To find these values and initialize the Azure Machine Learning variables with them, complete these steps:
 
 1. In the left menu, select **Settings**. Note the value for **WORKSPACE ID**.
 
-   ![The Azure Machine Learning Studio workspace ID](./media/linux-dsvm-walkthrough/workspace-id.png)
+   ![The Azure Machine Learning Studio (classic) workspace ID](./media/linux-dsvm-walkthrough/workspace-id.png)
 
 1. Select the **Authorization Tokens** tab. Note the value for **Primary Authorization Token**.
 
-   ![The Azure Machine Learning Studio primary authorization token](./media/linux-dsvm-walkthrough/workspace-token.png)
+   ![The Azure Machine Learning Studio (classic) primary authorization token](./media/linux-dsvm-walkthrough/workspace-token.png)
 1. Load the **AzureML** package, and then set values of the variables with your token and workspace ID in your R session on the DSVM:
 
         if(!require("AzureML")) install.packages("AzureML")

articles/machine-learning/studio/algorithm-choice.md

@@ -18,7 +18,7 @@ ms.date: 03/04/2019
 
 The answer to the question "What machine learning algorithm should I use?" is always "It depends." It depends on the size, quality, and nature of the data. It depends on what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them.
 
-Machine Learning Studio (classic) provides state-of-the-art algorithms, such as Scalable Boosted Decision trees, Bayesian Recommendation systems, Deep Neural Networks, and Decision Jungles developed at Microsoft Research. Scalable open-source machine learning packages, like Vowpal Wabbit, are also included. The classic version of Machine Learning Studio supports machine learning algorithms for multiclass and binary classification, regression, and clustering. See the complete list of [Machine Learning Modules](/azure/machine-learning/studio-module-reference/index).
+Machine Learning Studio (classic) provides state-of-the-art algorithms, such as Scalable Boosted Decision trees, Bayesian Recommendation systems, Deep Neural Networks, and Decision Jungles developed at Microsoft Research. Scalable open-source machine learning packages, like Vowpal Wabbit, are also included. Machine Learning Studio (classic) supports machine learning algorithms for multiclass and binary classification, regression, and clustering. See the complete list of [Machine Learning Modules](/azure/machine-learning/studio-module-reference/index).
 The documentation provides some information about each algorithm and how to tune parameters to optimize the algorithm for your use.  
 
 
@@ -55,7 +55,7 @@ can, it uses that pattern to make predictions for unlabeled testing
 data—tomorrow's prices.
 
 Supervised learning is a popular and useful type of machine learning. With one
-exception, all the modules in the classic version of Azure Machine Learning Studio are supervised learning
+exception, all the modules in Azure Machine Learning Studio (classic) are supervised learning
 algorithms. There are several specific types of supervised learning that
 are represented within Azure Machine Learning Studio (classic): classification, regression, and anomaly
 detection.
@@ -154,7 +154,7 @@ the most trial and error to find a good combination.
 
 Alternatively, there is a [parameter
 sweeping](algorithm-parameters-optimize.md)
-module block in the classic version of Azure Machine Learning Studio that automatically tries all parameter
+module block in Azure Machine Learning Studio (classic) that automatically tries all parameter
 combinations at whatever granularity you choose. While this is a great
 way to make sure you've spanned the parameter space, the time required
 to train a model increases exponentially with the number of parameters.
@@ -304,7 +304,7 @@ Neural networks are brain-inspired learning algorithms covering
 and
 [regression](/azure/machine-learning/studio-module-reference/neural-network-regression)
 problems. They come in an infinite variety, but the neural networks
-within the classic version of Azure Machine Learning Studio are all of the form of directed acyclic graphs. That
+within Azure Machine Learning Studio (classic) are all of the form of directed acyclic graphs. That
 means that input features are passed forward (never backward) through a
 sequence of layers before being turned into outputs. In each layer,
 inputs are weighted in various combinations, summed, and passed on to
@@ -373,7 +373,7 @@ boundary are unusual enough to be noteworthy.
 Bayesian methods have a highly desirable quality: they avoid
 overfitting. They do this by making some assumptions beforehand about
 the likely distribution of the answer. Another byproduct of this
-approach is that they have very few parameters. The classic version of Azure Machine Learning Studio has
+approach is that they have very few parameters. Azure Machine Learning Studio (classic) has
 Bayesian algorithms for both classification ([Two-class Bayes' point
 machine](/azure/machine-learning/studio-module-reference/two-class-bayes-point-machine))
 and regression ([Bayesian linear
@@ -423,7 +423,7 @@ properties are determined by the two-class classifiers used.
 ***A pair of two-class classifiers combine to form a three-class
 classifier***
 
-The classic version of Azure Machine Learning Studio also includes access to a powerful machine learning framework
+Azure Machine Learning Studio (classic) also includes access to a powerful machine learning framework
 under the title of [Vowpal
 Wabbit](/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-7-4-model).
 VW defies categorization here, since it can learn both classification
@@ -445,4 +445,4 @@ several languages.
 
 * For a list by category of all the machine learning algorithms available in Machine Learning Studio (classic), see [Initialize Model](/azure/machine-learning/studio-module-reference/machine-learning-initialize-model) in the Machine Learning Studio (classic) Algorithm and Module Help.
 
-* For a complete alphabetical list of algorithms and modules in the classic version of Machine Learning Studio, see [A-Z list of Machine Learning Studio (classic) modules](/azure/machine-learning/studio-module-reference/a-z-module-list) in Machine Learning Studio (classic) Algorithm and Module Help.
+* For a complete alphabetical list of algorithms and modules in Machine Learning Studio (classic), see [A-Z list of Machine Learning Studio (classic) modules](/azure/machine-learning/studio-module-reference/a-z-module-list) in Machine Learning Studio (classic) Algorithm and Module Help.

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

@@ -18,7 +18,7 @@ This topic describes how to choose the right hyperparameter set for an algorithm
 
 
 
-There are various ways to do model selection. In machine learning, cross-validation is one of the most widely used methods for model selection, and it is the default model selection mechanism in the classic version of Azure Machine Learning Studio. Because the classic version of Azure Machine Learning Studio supports both R and Python, you can always implement their own model selection mechanisms by using either R or Python.
+There are various ways to do model selection. In machine learning, cross-validation is one of the most widely used methods for model selection, and it is the default model selection mechanism in Azure Machine Learning Studio (classic). Because Azure Machine Learning Studio (classic) supports both R and Python, you can always implement their own model selection mechanisms by using either R or Python.
 
 There are four steps in the process of finding the best parameter set:
 
@@ -27,7 +27,7 @@ There are four steps in the process of finding the best parameter set:
 3. **Define the metric**: Decide what metric to use for determining the best set of parameters, such as accuracy, root mean squared error, precision, recall, or f-score.
 4. **Train, evaluate, and compare**: For each unique combination of the parameter values, cross-validation is carried out by and based on the error metric you define. After evaluation and comparison, you can choose the best-performing model.
 
-The following image illustrates shows how this can be achieved in the classic version of Azure Machine Learning Studio.
+The following image illustrates how this can be achieved in Azure Machine Learning Studio (classic).
 
 ![Find the best parameter set](./media/algorithm-parameters-optimize/fig1.png)
 

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

@@ -66,7 +66,7 @@ An interesting addition here is big data analytics. Today's telecommunication an
 
 
 ## Implementing the modeling archetype in Machine Learning Studio (classic)
-Given the problem described, what is the best way to implement an integrated modeling and scoring approach? In this section, we will demonstrate how we accomplished this by using the classic version of Azure Machine Learning Studio.  
+Given the problem described, what is the best way to implement an integrated modeling and scoring approach? In this section, we will demonstrate how we accomplished this by using Azure Machine Learning Studio (classic).  
 
 The multi-model approach is a must when designing a global archetype for churn. Even the scoring (predictive) part of the approach should be multi-model.  
 
@@ -131,7 +131,7 @@ We also submitted the scoring dataset to a comparable model built by using the d
 In this section, we present our findings about the accuracy of the models, based on the scoring dataset.  
 
 ### Accuracy and precision of scoring
-Generally, the implementation in the classic version of Azure Machine Learning Studio is behind SAS in accuracy by about 10-15% (Area Under Curve or AUC).  
+Generally, the implementation in Azure Machine Learning Studio (classic) is behind SAS in accuracy by about 10-15% (Area Under Curve or AUC).  
 
 However, the most important metric in churn is the misclassification rate: that is, of the top N churners as predicted by the classifier, which of them actually did **not** churn, and yet received special treatment? The following diagram compares this misclassification rate for all the models:  
 
@@ -196,13 +196,13 @@ This important observation is often overlooked in business, which generally pref
 
 However, the promise of self-service analytics by using Machine Learning Studio (classic) is that the four categories of information, graded by division or department, become a valuable source for machine learning about churn.  
 
-Another exciting capability coming in the classic version of Azure Machine Learning Studio is the ability to add a custom module to the repository of predefined modules that are already available. This capability, essentially, creates an opportunity to select libraries and create templates for vertical markets. It is an important differentiator of the classic version of Azure Machine Learning Studio in the market place.  
+Another exciting capability coming in Azure Machine Learning Studio (classic) is the ability to add a custom module to the repository of predefined modules that are already available. This capability, essentially, creates an opportunity to select libraries and create templates for vertical markets. It is an important differentiator of Azure Machine Learning Studio (classic) in the market place.  
 
 We hope to continue this topic in the future, especially related to big data analytics.
 
 
 ## Conclusion
-This paper describes a sensible approach to tackling the common problem of customer churn by using a generic framework. We considered a prototype for scoring models and implemented it by using the classic version of Azure Machine Learning Studio. Finally, we assessed the accuracy and performance of the prototype solution with regard to comparable algorithms in SAS.  
+This paper describes a sensible approach to tackling the common problem of customer churn by using a generic framework. We considered a prototype for scoring models and implemented it by using Azure Machine Learning Studio (classic). Finally, we assessed the accuracy and performance of the prototype solution with regard to comparable algorithms in SAS.  
 
 
 

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

@@ -51,7 +51,7 @@ Additionally, Net# supports the following four kinds of advanced connection bund
 
 ## Supported customizations
 
-The architecture of neural network models that you create in the classic version of Azure Machine Learning Studio can be extensively customized by using Net#. You can:
+The architecture of neural network models that you create in Azure Machine Learning Studio (classic) can be extensively customized by using Net#. You can:
 
 + Create hidden layers and control the number of nodes in each layer.
 + Specify how layers are to be connected to each other.

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

@@ -30,8 +30,8 @@ Download this easy-to-understand infographic overview of machine learning basics
 ## More help with algorithms for beginners and advanced users
 
 * For a deeper discussion of the different types of machine learning algorithms, how they're used, and how to choose the right one for your solution, see [How to choose algorithms for Microsoft Azure Machine Learning Studio (classic)](algorithm-choice.md).
-* For a list by category of all the machine learning algorithms available in the classic version of Machine Learning Studio, see [Initialize Model][initialize-model] in the Machine Learning Studio (classic) Algorithm and Module Help.
-* For a complete alphabetical list of algorithms and modules in classic version of Machine Learning Studio, see [A-Z list of Machine Learning Studio (classic) modules][a-z-list] in Machine Learning Studio (classic) Algorithm and Module Help.
+* For a list by category of all the machine learning algorithms available in Machine Learning Studio (classic), see [Initialize Model][initialize-model] in the Machine Learning Studio (classic) Algorithm and Module Help.
+* For a complete alphabetical list of algorithms and modules in Machine Learning Studio (classic), see [A-Z list of Machine Learning Studio (classic) modules][a-z-list] in Machine Learning Studio (classic) Algorithm and Module Help.
 * For an overview of the Azure AI Gallery and the many community-generated resources available there, see [Share and discover resources in the Azure AI Gallery](gallery-how-to-use-contribute-publish.md).
 
 

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

@@ -27,7 +27,7 @@ You can find more information about how to create and deploy a Machine Learning
 ## Overview
 With the Azure Machine Learning Web service, an external application communicates with a Machine Learning workflow scoring model in real time. A Machine Learning Web service call returns prediction results to an external application. To make a Machine Learning Web service call, you pass an API key that is created when you deploy a prediction. The Machine Learning Web service is based on REST, a popular architecture choice for web programming projects.
 
-The classic version of Azure Machine Learning Studio has two types of services:
+Azure Machine Learning Studio (classic) has two types of services:
 
 * Request-Response Service (RRS) – A low latency, highly scalable service that provides an interface to the stateless models created and deployed from the Machine Learning Studio (classic).
 * Batch Execution Service (BES) – An asynchronous service that scores a batch for data records.

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

@@ -15,7 +15,7 @@ ms.date: 03/28/2017
 
 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 the classic version of Studio 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.
+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.
 
 You can see an example of this process in [Tutorial 1: Predict credit risk](tutorial-part1-credit-risk.md).