freshness & add links to quickstart

Author: sdgilley

articles/machine-learning/concept-compute-instance.md

@@ -9,7 +9,8 @@ ms.custom: event-tier1-build-2022
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 09/22/2021
+ms.reviewer: sgilley
+ms.date: 10/19/2022
 #Customer intent: As a data scientist, I want to know what a compute instance is and how to use it for Azure Machine Learning.
 ---
 
@@ -89,7 +90,7 @@ Python packages are all installed in the **Python 3.8 - AzureML** environment. C
 
 ## Accessing files
 
-Notebooks and R scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your “User files” directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
+Notebooks and Python scripts are stored in the default storage account of your workspace in Azure file share.  These files are located under your “User files” directory. This storage makes it easy to share notebooks between compute instances. The storage account also keeps your notebooks safely preserved when you stop or delete a compute instance.
 
 The Azure file share account of your workspace is mounted as a drive on the compute instance. This drive is the default working directory for Jupyter, Jupyter Labs, and RStudio. This means that the notebooks and other files you create in Jupyter, JupyterLab, or RStudio are automatically stored on the file share and available to use in other compute instances as well.
 
@@ -101,17 +102,18 @@ Writing small files can be slower on network drives than writing to the compute
 
 Do not store training data on the notebooks file share. You can use the `/tmp` directory on the compute instance for your temporary data.  However, do not write very large files of data on the OS disk of the compute instance. OS disk on compute instance has 128 GB capacity. You can also store temporary training data on temporary disk mounted on /mnt. Temporary disk size is configurable based on the VM size chosen and can store larger amounts of data if a higher size VM is chosen. You can also mount [datastores and datasets](v1/concept-azure-machine-learning-architecture.md#datasets-and-datastores). Any software packages you install are saved on the OS disk of compute instance. Please note customer managed key encryption is currently not supported for OS disk. The OS disk for compute instance is encrypted with Microsoft-managed keys. 
 
-### Create
+## Create
+
+Follow the steps in the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md) to create a basic compute instance.  
+
+For more options, see [create a new compute instance](how-to-create-manage-compute-instance.md?tabs=azure-studio#create).
 
 As an administrator, you can **[create a compute instance for others in the workspace (preview)](how-to-create-manage-compute-instance.md#create-on-behalf-of-preview)**.
 
 You can also **[use a setup script (preview)](how-to-customize-compute-instance.md)** for an automated way to customize and configure the compute instance.
 
-To create a compute instance for yourself, use your workspace in Azure Machine Learning studio, [create a new compute instance](how-to-create-manage-compute-instance.md?tabs=azure-studio#create) from either the **Compute** section or in the **Notebooks** section when you are ready to run one of your notebooks.
-
-You can also create an instance
-* Directly from the [integrated notebooks experience](tutorial-train-deploy-notebook.md#azure)
-* In Azure portal
+Other ways to create a compute instance:
+* Directly from the integrated notebooks experience.
 * From Azure Resource Manager template. For an example template, see the [create an Azure Machine Learning compute instance template](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/machine-learning-compute-create-computeinstance).
 * With [Azure Machine Learning SDK](how-to-create-manage-compute-instance.md?tabs=python#create)
 * From the [CLI extension for Azure Machine Learning](how-to-create-manage-compute-instance.md?tabs=azure-cli#create)
@@ -139,5 +141,5 @@ You can use compute instance as a local inferencing deployment target for test/d
 
 ## Next steps
 
-* [Create and manage a compute instance](how-to-create-manage-compute-instance.md)
+* [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md).
 * [Tutorial: Train your first ML model](tutorial-1st-experiment-sdk-train.md) shows how to use a compute instance with an integrated notebook.

articles/machine-learning/concept-compute-target.md

@@ -8,7 +8,8 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 10/21/2021
+ms.reviewer: sgilley
+ms.date: 10/19/2022
 ms.custom: ignite-fall-2021, event-tier1-build-2022, cliv2
 #Customer intent: As a data scientist, I want to understand what a compute target is and why I need it.
 ---
@@ -20,27 +21,26 @@ A *compute target* is a designated compute resource or environment where you run
 In a typical model development lifecycle, you might:
 
 1. Start by developing and experimenting on a small amount of data. At this stage, use your local environment, such as a local computer or cloud-based virtual machine (VM), as your compute target.
-1. Scale up to larger data, or do [distributed training](how-to-train-distributed-gpu.md) by using one of these [training compute targets](#train).
-1. After your model is ready, deploy it to a web hosting environment with one of these [deployment compute targets](#deploy).
+1. Scale up to larger data, or do [distributed training](how-to-train-distributed-gpu.md) by using one of these [training compute targets](#training-compute-targets).
+1. After your model is ready, deploy it to a web hosting environment with one of these [deployment compute targets](#compute-targets-for-inference).
 
 The compute resources you use for your compute targets are attached to a [workspace](concept-workspace.md). Compute resources other than the local machine are shared by users of the workspace.
 
-## <a name="train"></a> Training compute targets
+## Training compute targets
 
 Azure Machine Learning has varying support across different compute targets. A typical model development lifecycle starts with development or experimentation on a small amount of data. At this stage, use a local environment like your local computer or a cloud-based VM. As you scale up your training on larger datasets or perform [distributed training](how-to-train-distributed-gpu.md), use Azure Machine Learning compute to create a single- or multi-node cluster that autoscales each time you submit a job. You can also attach your own compute resource, although support for different scenarios might vary.
 
 [!INCLUDE [aml-compute-target-train](../../includes/aml-compute-target-train.md)]
 
 
-## <a name="deploy"></a> Compute targets for inference
+## Compute targets for inference
 
 When performing inference, Azure Machine Learning creates a Docker container that hosts the model and associated resources needed to use it. This container is then used in a compute target.
 
 [!INCLUDE [aml-deploy-target](../../includes/aml-compute-target-deploy.md)]
 
 Learn [where and how to deploy your model to a compute target](how-to-deploy-managed-online-endpoints.md).
 
-<a name="amlcompute"></a>
 ## Azure Machine Learning compute (managed)
 
 A managed compute resource is created and managed by Azure Machine Learning. This compute is optimized for machine learning workloads. Azure Machine Learning compute clusters and [compute instances](concept-compute-instance.md) are the only managed computes.

articles/machine-learning/how-to-create-attach-compute-cluster.md

@@ -10,7 +10,7 @@ ms.custom: devx-track-azurecli, cliv2, sdkv1, event-tier1-build-2022
 ms.author: sgilley
 author: sdgilley
 ms.reviewer: sgilley
-ms.date: 09/21/2022
+ms.date: 10/19/2022
 ---
 
 # Create an Azure Machine Learning compute cluster
@@ -77,6 +77,10 @@ The dedicated cores per region per VM family quota and total regional quota, whi
 [!INCLUDE [min-nodes-note](../../includes/machine-learning-min-nodes.md)]
 
 The compute autoscales down to zero nodes when it isn't used.   Dedicated VMs are created to run your jobs as needed.
+
+The fastest way to create a compute cluster is to follow the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md). 
+
+Or use the following examples to create a compute cluster with more options:
 
 # [Python SDK](#tab/python)
 

articles/machine-learning/how-to-create-manage-compute-instance.md

@@ -10,7 +10,7 @@ ms.topic: how-to
 author: swatig007
 ms.author: swatig
 ms.reviewer: sgilley
-ms.date: 09/21/2022
+ms.date: 10/19/2022
 ---
 
 # Create and manage an Azure Machine Learning compute instance
@@ -21,7 +21,7 @@ ms.date: 09/21/2022
 > * [v1](v1/how-to-create-manage-compute-instance.md)
 > * [v2 (current version)](how-to-create-manage-compute-instance.md)
 
-Learn how to create and manage a [compute instance](concept-compute-instance.md) in your Azure Machine Learning workspace.
+Learn how to create and manage a [compute instance](concept-compute-instance.md) in your Azure Machine Learning workspace. 
 
 Use a compute instance as your fully configured and managed development environment in the cloud. For development and testing, you can also use the instance as a [training compute target](concept-compute-target.md#train).   A compute instance can run multiple jobs in parallel and has a job queue. As a development environment, a compute instance can't be shared with other users in your workspace.
 
@@ -62,7 +62,9 @@ Creating a compute instance is a one time process for your workspace. You can re
 
 The dedicated cores per region per VM family quota and total regional quota, which applies to compute instance creation, is unified and shared with Azure Machine Learning training compute cluster quota. Stopping the compute instance doesn't release quota to ensure you'll be able to restart the compute instance. It isn't possible to change the virtual machine size of compute instance once it's created.
 
-The following example demonstrates how to create a compute instance:
+The fastest way to create a compute instance is to follow the [Quickstart: Create workspace resources you need to get started with Azure Machine Learning](quickstart-create-resources.md). 
+
+Or use the following examples to create a compute instance with more options:
 
 # [Python SDK](#tab/python)
 
@@ -240,7 +242,6 @@ You can also create your own custom Azure policy. For example, if the below poli
 }
 ```
 
-
 ## Create on behalf of (preview)
 
 As an administrator, you can create a compute instance on behalf of a data scientist and assign the instance to them with: