Merge pull request #273926 from sdgilley/sdg-feedback

feedback: link to other ways to create workspace

Author: prmerger-automator[bot]

articles/machine-learning/quickstart-create-resources.md

@@ -17,16 +17,20 @@ ms.custom: mode-other
 
 # Tutorial: Create resources you need to get started
 
-In this tutorial, you will create the resources you need to start working with Azure Machine Learning. 
+In this tutorial, you'll create the resources you need to start working with Azure Machine Learning.
 
 > [!div class="checklist"]
->* A *workspace*.  To use Azure Machine Learning, you'll first need a workspace.  The workspace is the central place to view and manage all the artifacts and resources you create. 
->* A *compute instance*.  A compute instance is a pre-configured cloud-computing resource that you can use to train, automate, manage, and track machine learning models. A compute instance is the quickest way to start using the Azure Machine Learning SDKs and CLIs. You'll use it to run Jupyter notebooks and Python scripts in the rest of the tutorials.
+>* A *workspace*. To use Azure Machine Learning, you'll first need a workspace. The workspace is the central place to view and manage all the artifacts and resources you create. 
+>* A *compute instance*. A compute instance is a pre-configured cloud-computing resource that you can use to train, automate, manage, and track machine learning models. A compute instance is the quickest way to start using the Azure Machine Learning SDKs and CLIs. You'll use it to run Jupyter notebooks and Python scripts in the rest of the tutorials.
 >
 
-In this tutorial, you'll create the your resources in [Azure Machine Learning studio](https://ml.azure.com).  For more ways to create a workspace, see [Create a workspace](concept-workspace.md#create-a-workspace). For more ways to create a compute instance, see [Create a compute instance](how-to-create-compute-instance.md).
+In this tutorial, you'll create your resources in [Azure Machine Learning studio](https://ml.azure.com). 
 
-This video shows you how to create a workspace and compute instance in Azure Machine Learning studio.  The steps are also described in the sections below.
+Other ways to create a workspace are via the [Azure portal or SDK](how-to-manage-workspace.md), [the CLI](how-to-manage-workspace-cli.md), [Azure PowerShell](how-to-manage-workspace-powershell.md),  or [the Visual Studio Code extension](how-to-setup-vs-code.md).
+
+For other ways to create a compute instance, see [Create a compute instance](how-to-create-compute-instance.md).
+
+This video shows you how to create a workspace and compute instance in Azure Machine Learning studio. The steps are also described in the sections below.
 > [!VIDEO https://learn-video.azurefd.net/vod/player?id=a0e901d2-e82a-4e96-9c7f-3b5467859969]
 
 ## Prerequisites
@@ -49,7 +53,7 @@ If you don't yet have a workspace, create one now:
    ---|---
    Workspace name |Enter a unique name that identifies your workspace. Names must be unique across the resource group. Use a name that's easy to recall and to differentiate from workspaces created by others. The workspace name is case-insensitive.
    Subscription |Select the Azure subscription that you want to use.
-   Resource group | Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. You need *contributor* or *owner* role to use an existing resource group.  For more information about access, see [Manage access to an Azure Machine Learning workspace](how-to-assign-roles.md).
+   Resource group | Use an existing resource group in your subscription or enter a name to create a new resource group. A resource group holds related resources for an Azure solution. You need *contributor* or *owner* role to use an existing resource group. For more information about access, see [Manage access to an Azure Machine Learning workspace](how-to-assign-roles.md).
    Region | Select the Azure region closest to your users and the data resources to create your workspace.
 1. Select **Create** to create the workspace
 
@@ -78,13 +82,13 @@ The studio is your web portal for Azure Machine Learning. This portal combines n
 
 Review the parts of the studio on the left-hand navigation bar:
 
-* The **Authoring** section of the studio contains multiple ways to get started in creating machine learning models.  You can:
+* The **Authoring** section of the studio contains multiple ways to get started in creating machine learning models. You can:
 
     * **Notebooks** section allows you to create Jupyter Notebooks, copy sample notebooks, and run notebooks and Python scripts.
     * **Automated ML** steps you through creating a machine learning model without writing code.
     * **Designer** gives you a drag-and-drop way to build models using prebuilt components.
 
-* The **Assets** section of the studio helps you keep track of the assets you create as you run your jobs.  If you have a new workspace, there's nothing in any of these sections yet.
+* The **Assets** section of the studio helps you keep track of the assets you create as you run your jobs. If you have a new workspace, there's nothing in any of these sections yet.
 
 * The **Manage** section of the studio lets you create and manage compute and external services you link to your workspace. It's also where you can create and manage a **Data labeling** project.
 
@@ -100,14 +104,14 @@ Use the sample notebooks available in studio to help you learn about how to trai
 :::image type="content" source="media/quickstart-create-resources/samples.png" alt-text="Screenshot shows sample notebooks.":::
 
 * Use notebooks in the **SDK v2** folder for examples that show the current version of the SDK, v2.
-* These notebooks are read-only, and are updated periodically.  
-* When you open a notebook, select the **Clone this notebook** button at the top to add your copy of the notebook and any associated files into your own files.  A new folder with the notebook is created for you in the **Files** section.
+* These notebooks are read-only, and are updated periodically. 
+* When you open a notebook, select the **Clone this notebook** button at the top to add your copy of the notebook and any associated files into your own files. A new folder with the notebook is created for you in the **Files** section.
 
 ## Create a new notebook
 
-When you clone a notebook from **Samples**, a copy is added to your files and you can start running or modifying it.  Many of the tutorials will mirror these sample notebooks.  
+When you clone a notebook from **Samples**, a copy is added to your files and you can start running or modifying it. Many of the tutorials mirror these sample notebooks. 
 
-But you could also create a new, empty notebook, then copy/paste code from a tutorial into the notebook.  To do so:
+But you could also create a new, empty notebook, then copy/paste code from a tutorial into the notebook. To do so:
 
 1. Still in the **Notebooks** section, select **Files** to go back to your files,
 1. Select **+** to add files.
@@ -124,7 +128,7 @@ If you plan to continue now to other tutorials, skip to [Next steps](#next-steps
 
 If you're not going to use it now, stop the compute instance:
 
-1. In the studio, on the left, select **Compute**.
+1. In the studio, on the left menu, select **Compute**.
 1. In the top tabs, select **Compute instances**
 1. Select the compute instance in the list.
 1. On the top toolbar, select **Stop**.
@@ -137,7 +141,7 @@ If you're not going to use it now, stop the compute instance:
 
 You now have an Azure Machine Learning workspace, which contains a compute instance to use for your development environment.
 
-Continue on to learn how to use the compute instance to run notebooks and scripts in the Azure Machine Learning cloud.  
+Continue on to learn how to use the compute instance to run notebooks and scripts in the Azure Machine Learning cloud. 
 
 > [!div class="nextstepaction"]
 > [Quickstart: Get to know Azure Machine Learning](tutorial-azure-ml-in-a-day.md) 
@@ -146,7 +150,7 @@ Use your compute instance with the following tutorials to train and deploy a mod
 
 |Tutorial  |Description  |
 |---------|---------|
-| [Upload, access and explore your data in Azure Machine Learning](tutorial-explore-data.md)     |  Store large data in the cloud and retrieve it from notebooks and scripts |
+| [Upload, access, and explore your data in Azure Machine Learning](tutorial-explore-data.md)     |  Store large data in the cloud and retrieve it from notebooks and scripts |
 | [Model development on a cloud workstation](tutorial-cloud-workstation.md) | Start prototyping and developing machine learning models |
 | [Train a model in Azure Machine Learning](tutorial-train-model.md) |    Dive in to the details of training a model     |
 | [Deploy a model as an online endpoint](tutorial-deploy-model.md)  |   Dive in to the details of deploying a model      |