Merge pull request #209394 from sdgilley/sdg-v2

BCS2022 · web-flow · commit 6e92b78f41d3 · 2022-08-29T10:30:39.000-07:00
Workspace concept update for v2
diff --git a/articles/machine-learning/concept-workspace.md b/articles/machine-learning/concept-workspace.md
@@ -9,7 +9,7 @@ ms.custom: event-tier1-build-2022
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 01/04/2022
+ms.date: 08/26/2022
 #Customer intent: As a data scientist, I want to understand the purpose of a workspace for Azure Machine Learning.
 ---
 
@@ -18,7 +18,7 @@ ms.date: 01/04/2022
 
 The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning.  The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model.  
 
-Once you have a model you like, you register it with the workspace. You then use the registered model and scoring scripts to deploy to Azure Container Instances, Azure Kubernetes Service, or to a field-programmable gate array (FPGA) as a REST-based HTTP endpoint.
+Once you have a model you like, you register it with the workspace. You then use the registered model and scoring scripts to deploy to an [online endpoint](concept-endpoints.md) as a REST-based HTTP endpoint.
 
 ## Taxonomy 
 
@@ -31,70 +31,65 @@ The diagram shows the following components of a workspace:
 + A workspace can contain [Azure Machine Learning compute instances](concept-compute-instance.md), cloud resources configured with the Python environment necessary to run Azure Machine Learning.
 
 + [User roles](how-to-assign-roles.md) enable you to share your workspace with other users, teams, or projects.
-+ [Compute targets](v1/concept-azure-machine-learning-architecture.md#compute-targets) are used to run your experiments.
-+ When you create the workspace, [associated resources](#resources) are also created for you.
-+ [Experiments](v1/concept-azure-machine-learning-architecture.md#experiments) are training runs you use to build your models.  
-+ [Pipelines](v1/concept-azure-machine-learning-architecture.md#ml-pipelines) are reusable workflows for training and retraining your model.
-+ [Datasets](v1/concept-azure-machine-learning-architecture.md#datasets-and-datastores) aid in management of the data you use for model training and pipeline creation.
++ [Compute targets](concept-compute-target.md) are used to run your experiments.
++ When you create the workspace, [associated resources](#associated-resources) are also created for you.
++ Jobs are training runs you use to build your models.  You can organize your jobs into Experiments.
++ [Pipelines](concept-ml-pipelines.md) are reusable workflows for training and retraining your model.
++ [Data assets](concept-data.md) aid in management of the data you use for model training and pipeline creation.
 + Once you have a model you want to deploy, you create a registered model.
-+ Use the registered model and a scoring script to create a [deployment endpoint](v1/concept-azure-machine-learning-architecture.md#endpoints).
++ Use the registered model and a scoring script to create an [online endpoint](concept-endpoints.md).
 
 ## Tools for workspace interaction
 
 You can interact with your workspace in the following ways:
 
-> [!IMPORTANT]
-> Tools marked (preview) below are currently in public preview.
-> The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. 
-> For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
-
 + On the web:
     + [Azure Machine Learning studio ](https://ml.azure.com) 
     + [Azure Machine Learning designer](concept-designer.md) 
 + In any Python environment with the [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml/intro).
 + On the command line using the Azure Machine Learning [CLI extension](how-to-configure-cli.md)
 + [Azure Machine Learning VS Code Extension](how-to-manage-resources-vscode.md#workspaces)
 
-
 ## Machine learning with a workspace
 
 Machine learning tasks read and/or write artifacts to your workspace.
 
-+ Run an experiment to train a model - writes experiment run results to the workspace.
++ Run an experiment to train a model - writes job run results to the workspace.
 + Use automated ML to train a model - writes training results to the workspace.
 + Register a model in the workspace.
 + Deploy a model - uses the registered model to create a deployment.
 + Create and run reusable workflows.
-+ View machine learning artifacts such as experiments, pipelines, models, deployments.
++ View machine learning artifacts such as jobs, pipelines, models, deployments.
 + Track and monitor models.
 
 ## Workspace management
 
 You can also perform the following workspace management tasks:
 
-| Workspace management task   | Portal              | Studio | Python SDK      | Azure CLI        | VS Code
-|---------------------------|---------|---------|------------|------------|------------|
-| Create a workspace        | **&check;**     | | **&check;** | **&check;** | **&check;** |
-| Manage workspace access    | **&check;**   || |  **&check;**    ||
-| Create and manage compute resources    | **&check;**   | **&check;** | **&check;** |  **&check;**   ||
-| Create a Notebook VM |   | **&check;** | |     ||
+| Workspace management task           | Portal      | Studio      | Python SDK  | Azure CLI   | VS Code     |
+|-------------------------------------|-------------|-------------|-------------|-------------|-------------|
+| Create a workspace                  | **&check;** | **&check;** | **&check;** | **&check;** | **&check;** |
+| Manage workspace access             | **&check;** |             |             | **&check;** |             |
+| Create and manage compute resources | **&check;** | **&check;** | **&check;** | **&check;** | **&check;** |
+| Create a compute instance           |             | **&check;** | **&check;** | **&check;** | **&check;** |
 
 > [!WARNING]
 > Moving your Azure Machine Learning workspace to a different subscription, or moving the owning subscription to a new tenant, is not supported. Doing so may cause errors.
 
-## <a name='create-workspace'></a> Create a workspace
+## Create a workspace
 
 There are multiple ways to create a workspace:  
 
-* Use the [Azure portal](quickstart-create-resources.md) for a point-and-click interface to walk you through each step.
-* Use the [Azure Machine Learning SDK for Python](how-to-manage-workspace.md?tabs=python#create-a-workspace) to create a workspace on the fly from Python scripts or Jupyter notebooks
+* Use [Azure Machine Learning studio](quickstart-create-resources.md) to quickly create a workspace with default settings.
+* Use the [Azure portal](how-to-manage-workspace.md?tabs=azure-portal#create-a-workspace) for a point-and-click interface with more options. 
+* Use the [Azure Machine Learning SDK for Python](how-to-manage-workspace.md?tabs=python#create-a-workspace) to create a workspace on the fly from Python scripts or Jupyter notebooks.
 * Use an [Azure Resource Manager template](how-to-create-workspace-template.md) or the [Azure Machine Learning CLI](how-to-configure-cli.md) when you need to automate or customize the creation with corporate security standards.
 * If you work in Visual Studio Code, use the [VS Code extension](how-to-manage-resources-vscode.md#create-a-workspace).
 
 > [!NOTE]
 > The workspace name is case-insensitive.
 
-## <a name="sub-resources"></a> Sub resources
+## Sub resources
 
 These sub resources are the main resources that are made in the AzureML workspace.
 
@@ -103,7 +98,7 @@ These sub resources are the main resources that are made in the AzureML workspac
 * Virtual Network: these help Azure resources communicate with one another, the internet, and other on-premises networks.
 * Bandwidth: encapsulates all outbound data transfers across regions.
 
-## <a name="resources"></a> Associated resources
+## Associated resources
 
 When you create a new workspace, it automatically creates several Azure resources that are used by the workspace:
 
@@ -135,17 +130,6 @@ When you create a new workspace, it automatically creates several Azure resource
 > [!NOTE]
 > You can instead use existing Azure resource instances when you create the workspace with the [Python SDK](how-to-manage-workspace.md?tabs=python#create-a-workspace) or the Azure Machine Learning CLI [using an ARM template](how-to-create-workspace-template.md).
 
-<a name="wheres-enterprise"></a>
-
-## What happened to Enterprise edition
-
-As of September 2020, all capabilities that were available in Enterprise edition workspaces are now also available in Basic edition workspaces. 
-New Enterprise workspaces can no longer be created.  Any SDK, CLI, or Azure Resource Manager calls that use the `sku` parameter will continue to work but a Basic workspace will be provisioned.
-
-Beginning December 21st, all Enterprise Edition workspaces will be automatically set to Basic Edition, which has the same capabilities. No downtime will occur during this process. On January 1, 2021, Enterprise Edition will be formally retired. 
-
-In either editions, customers are responsible for the costs of Azure resources consumed and will not need to pay any additional charges for Azure Machine Learning. Please refer to the [Azure Machine Learning pricing page](https://azure.microsoft.com/pricing/details/machine-learning/) for more details.
-
 ## Next steps
 
 To learn more about planning a workspace for your organization's requirements, see [Organize and set up Azure Machine Learning](/azure/cloud-adoption-framework/ready/azure-best-practices/ai-machine-learning-resource-organization).
diff --git a/articles/machine-learning/how-to-manage-workspace-cli.md b/articles/machine-learning/how-to-manage-workspace-cli.md
@@ -111,7 +111,7 @@ For more information on working with resource groups, see [az group](/cli/azure/
 
 ## Create a workspace
 
-When you deploy an Azure Machine Learning workspace, various other services are [required as dependent associated resources](./concept-workspace.md#resources). When you use the CLI to create the workspace, the CLI can either create new associated resources on your behalf or you could attach existing resources.
+When you deploy an Azure Machine Learning workspace, various other services are [required as dependent associated resources](./concept-workspace.md#associated-resources). When you use the CLI to create the workspace, the CLI can either create new associated resources on your behalf or you could attach existing resources.
 
 > [!IMPORTANT]
 > When attaching your own storage account, make sure that it meets the following criteria:
diff --git a/articles/machine-learning/how-to-manage-workspace-terraform.md b/articles/machine-learning/how-to-manage-workspace-terraform.md
@@ -49,7 +49,7 @@ Create the Terraform configuration file that declares the Azure provider:
 
 ## Deploy a workspace
 
-The following Terraform configurations can be used to create an Azure Machine Learning workspace. When you create an Azure Machine Learning workspace, various other services are required as dependencies. The template also specifies these [associated resources to the workspace](./concept-workspace.md#resources). Depending on your needs, you can choose to use the template that creates resources with either public or private network connectivity.
+The following Terraform configurations can be used to create an Azure Machine Learning workspace. When you create an Azure Machine Learning workspace, various other services are required as dependencies. The template also specifies these [associated resources to the workspace](./concept-workspace.md#associated-resources). Depending on your needs, you can choose to use the template that creates resources with either public or private network connectivity.
 
 # [Public network connectivity](#tab/publicworkspace)
 
diff --git a/articles/machine-learning/quickstart-create-resources.md b/articles/machine-learning/quickstart-create-resources.md
@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: quickstart
 author: sdgilley
 ms.author: sgilley
-ms.date: 10/21/2021
+ms.date: 08/26/2022
 adobe-target: true
 ms.custom: FY21Q4-aml-seo-hack, contperf-fy21q4, mode-other
 #Customer intent: As a data scientist, I want to create a workspace so that I can start to use Azure Machine Learning.
@@ -27,27 +27,36 @@ The workspace is the top-level resource for your machine learning activities, pr
 
 ## Create the workspace
 
-If you  already have a workspace, skip this section and continue to [Create a compute instance](#instance).
+If you  already have a workspace, skip this section and continue to [Create a compute instance](#create-compute-instance).
 
-If you don't yet have a workspace, create one now:
+If you don't yet have a workspace, create one now: 
+1. Sign in to [Azure Machine Learning studio](https://ml.azure.com)
+1. Select **Create workspace**
+1. Provide the following information to configure your new workspace:
 
-[!INCLUDE [aml-create-portal](../../includes/aml-create-in-portal.md)]
+   Field|Description 
+   ---|---
+   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).
+   Region | Select the Azure region closest to your users and the data resources to create your workspace.
+1. Select **Create** to create the workspace
 
-## <a name="instance"></a> Create compute instance
+## Create compute instance
 
 You could install Azure Machine Learning on your own computer.  But in this quickstart, you'll create an online compute resource that has a development environment already installed and ready to go.  You'll use this online machine, a *compute instance*, for your development environment to write and run code in Python scripts and Jupyter notebooks.
 
 Create a *compute instance* to use this development environment for the rest of the tutorials and quickstarts.
 
-1. If you didn't select **Go to workspace** in the previous section, sign in to [Azure Machine Learning studio](https://ml.azure.com) now, and select your workspace.
+1. If you didn't just create a workspace in the previous section, sign in to [Azure Machine Learning studio](https://ml.azure.com) now, and select your workspace.
 1. On the left side, select **Compute**.
 1. Select **+New** to create a new compute instance.
 1. Supply a name, Keep all the defaults on the first page.
 1. Select **Create**.
  
 In about two minutes, you'll see the **State** of the compute instance change from *Creating* to *Running*.  It's now ready to go.  
 
-## <a name="cluster"></a> Create compute clusters
+## Create compute clusters
 
 Next you'll create a compute cluster.  Clusters allow you to distribute a training or batch inference process across a cluster of CPU or GPU compute nodes in the cloud.
 
@@ -67,7 +76,7 @@ In less than a minute, the **State** of the cluster will change from *Creating*
 > [!NOTE]
 > When the cluster is created, it will have 0 nodes provisioned. The cluster *does not* incur costs until you submit a job. This cluster will scale down when it has been idle for 2,400 seconds (40 minutes).  This will give you time to use it in a few tutorials if you wish without waiting for it to scale back up.
 
-## <a name="studio"></a> Quick tour of the studio
+## Quick tour of the studio
 
 The studio is your web portal for Azure Machine Learning. This portal combines no-code and code-first experiences for an inclusive data science platform.
 
@@ -87,7 +96,7 @@ Review the parts of the studio on the left-hand navigation bar:
 
 [!INCLUDE [machine-learning-workspace-diagnostics](../../includes/machine-learning-workspace-diagnostics.md)]
 
-## <a name="clean-up"></a>Clean up resources
+## Clean up resources
 
 If you plan to continue now to the next tutorial, skip to [Next steps](#next-steps).
 
diff --git a/articles/machine-learning/v1/how-to-manage-workspace-cli.md b/articles/machine-learning/v1/how-to-manage-workspace-cli.md
@@ -95,7 +95,7 @@ For more information on working with resource groups, see [az group](/cli/azure/
 
 ## Create a workspace
 
-When you deploy an Azure Machine Learning workspace, various other services are [required as dependent associated resources](../concept-workspace.md#resources). When you use the CLI to create the workspace, the CLI can either create new associated resources on your behalf or you could attach existing resources.
+When you deploy an Azure Machine Learning workspace, various other services are [required as dependent associated resources](../concept-workspace.md#associated-resources). When you use the CLI to create the workspace, the CLI can either create new associated resources on your behalf or you could attach existing resources.
 
 > [!IMPORTANT]
 > When attaching your own storage account, make sure that it meets the following criteria:
