recommend VS Code for the Web

Author: sdgilley

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

@@ -52,11 +52,13 @@ A compute instance is a fully managed cloud-based workstation optimized for your
 
 Azure Machine Learning compute instance enables you to author, train, and deploy models in a fully integrated notebook experience in your workspace.
 
-You can run Jupyter notebooks in [VS Code](https://techcommunity.microsoft.com/t5/azure-ai/power-your-vs-code-notebooks-with-azml-compute-instances/ba-p/1629630) using compute instance as the remote server with no SSH needed. You can also enable VS Code integration through [remote SSH extension](https://devblogs.microsoft.com/python/enhance-your-azure-machine-learning-experience-with-the-vs-code-extension/).
+You can run notebooks from [your Azure Machine Learning workspace](./how-to-run-jupyter-notebooks.md), [Jupyter](https://jupyter.org/), [JupyterLab](https://jupyterlab.readthedocs.io) or [VS Code for the Web](./how-to-launch-vs-code-remote.md).
+ 
+We recommend you try VS Code for the Web to take advantage of the rich development environment it provides.  VS Code (Web) gives you many of the features of VS Code Desktop that you love, including search and syntax highlighting while browsing and editing.  For more information about using VS Code for the Web, see [Launch Visual Studio Code integrated with Azure Machine Learning (preview)](how-to-launch-vs-code-remote.md) and [Work in VS Code remotely connected to a compute instance (preview)](how-to-work-in-vs-code-remote.md).
 
 You can [install packages](how-to-access-terminal.md#install-packages) and [add kernels](how-to-access-terminal.md#add-new-kernels) to your compute instance.
 
-Following tools and environments are already installed on the compute instance:
+The following tools and environments are already installed on the compute instance:
 
 |General tools & environments|Details|
 |----|:----:|

articles/machine-learning/how-to-run-jupyter-notebooks.md

@@ -16,9 +16,13 @@ ms.date: 02/28/2022
 
 # Run Jupyter notebooks in your workspace
 
-Learn how to run your Jupyter notebooks directly in your workspace in Azure Machine Learning studio. While you can launch [Jupyter](https://jupyter.org/) or [JupyterLab](https://jupyterlab.readthedocs.io), you can also edit and run your notebooks without leaving the workspace.
+This article shows how to run your Jupyter notebooks inside your workspace of Azure Machine Learning studio.  There are other ways to run the notebook as well: [Jupyter](https://jupyter.org/), [JupyterLab](https://jupyterlab.readthedocs.io) or [VS Code for the Web](./how-to-launch-vs-code-remote.md).
+ 
+We recommend you try VS Code for the Web to take advantage of the rich development environment it provides.  VS Code for the Web gives you many of the features of VS Code Desktop that you love, including search and syntax highlighting while browsing and editing.  For more information about using VS Code for the Web, see [Launch Visual Studio Code integrated with Azure Machine Learning (preview)](how-to-launch-vs-code-remote.md) and [Work in VS Code remotely connected to a compute instance (preview)](how-to-work-in-vs-code-remote.md).
+
+No matter which solution you use to run the notebook, you'll have access to all the files from your workspace. For information on how to create and manage files, including notebooks, see [Create and manage files in your workspace](how-to-manage-files.md).
 
-For information on how to create and manage files, including notebooks, see [Create and manage files in your workspace](how-to-manage-files.md).
+This rest of this article shows the experience for running the notebook directly in studio.
 
 > [!IMPORTANT]
 > Features marked as (preview) are 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.