Merge pull request #89334 from greazer/greazer-extension-updates

Updates for Azure ML Extension docs

Author: Jak-MS

articles/machine-learning/service/concept-workspace.md

@@ -84,7 +84,7 @@ There are multiple ways to create a workspace.
 * Use the [Azure portal](how-to-manage-workspace.md) for a point-and-click interface to walk you through each step.
 * Use the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py#workspace) to create a workspace on the fly from Python scripts or Jupiter notebooks
 * Use an [Azure Resource Manager template](how-to-create-workspace-template.md) or the [Azure Machine Learning CLI](reference-azure-machine-learning-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-vscode-tools.md#get-started-with-azure-machine-learning).
+* If you work in Visual Studio Code, use the [VS Code extension](how-to-vscode-tools.md#get-started-with-azure-machine-learning-for-visual-studio-code).
 
 ## <a name="resources"></a> Associated resources
 

articles/machine-learning/service/how-to-configure-environment.md

@@ -31,7 +31,7 @@ This article also provides additional usage tips for the following tools:
 
 * [Jupyter Notebooks](#jupyter): If you're already using the Jupyter Notebook, the SDK has some extras that you should install.
 
-* [Visual Studio Code](#vscode): If you use Visual Studio Code, it has some useful extensions that you can install.
+* [Visual Studio Code](#vscode): If you use Visual Studio Code, the [Azure Machine Learning extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai) includes extensive language support for Python as well as features to make working with the Azure Machine Learning service much more convenient and productive.
 
 ## Prerequisites
 
@@ -71,7 +71,7 @@ The notebook VM is:
 
 + **Customizable**. While a managed and secure VM offering, you retain full access to the hardware capabilities and customize it to your heart’s desire. For example, quickly create the latest NVidia V100 powered VM to perform step-by-step debugging of novel Neural Network architecture.
 
-To stop incurring notebook VM charges, [stop the notebook VM](tutorial-1st-experiment-sdk-train.md#clean-up-resources). 
+To stop incurring notebook VM charges, [stop the notebook VM](tutorial-1st-experiment-sdk-train.md#clean-up-resources).
 
 ## <a id="dsvm"></a>Data Science Virtual Machine
 
@@ -85,7 +85,7 @@ The DSVM is a customized virtual machine (VM) image. It's designed for data scie
 
 The Azure Machine Learning SDK works on either the Ubuntu or Windows version of the DSVM. But if you plan to use the DSVM as a compute target as well, only Ubuntu is supported.
 
-To use the DSVM as a development environment, do the following:
+To use the DSVM as a development environment:
 
 1. Create a DSVM in either of the following environments:
 
@@ -146,9 +146,9 @@ For more information, see [Data Science Virtual Machines](https://azure.microsof
 
 ## <a id="local"></a>Local computer
 
-When you're using a local computer (which might also be a remote virtual machine), create an Anaconda environment and install the SDK by doing the following:
+When you're using a local computer (which might also be a remote virtual machine), create an Anaconda environment and install the SDK. Here's an example:
 
-1. Download and install [Anaconda](https://www.anaconda.com/distribution/#download-section) (Python 3.7 version)  if you don't already have it.
+1. Download and install [Anaconda](https://www.anaconda.com/distribution/#download-section) (Python 3.7 version) if you don't already have it.
 
 1. Open an Anaconda prompt and create an environment with the following commands:
 
@@ -180,10 +180,10 @@ When you're using a local computer (which might also be a remote virtual machine
 
 1. Use the following commands to install packages:
 
-    This command installs the base Azure Machine Learning SDK with notebook and automl extras. The `automl` extra is a large install, and can be removed from the brackets if you don't intend to run automated machine learning experiments. The `automl` extra also includes the Azure Machine Learning Data Prep SDK by default as a dependency.
+    This command installs the base Azure Machine Learning SDK with notebook and `automl` extras. The `automl` extra is a large install, and can be removed from the brackets if you don't intend to run automated machine learning experiments. The `automl` extra also includes the Azure Machine Learning Data Prep SDK by default as a dependency.
 
     ```shell
-    pip install azureml-sdk[notebooks,automl]
+    pip install azureml-sdk[notebooks, automl]
     ```
 
    > [!NOTE]
@@ -216,14 +216,16 @@ When you're using a local computer (which might also be a remote virtual machine
 
 Jupyter Notebooks are part of the [Jupyter Project](https://jupyter.org/). They provide an interactive coding experience where you create documents that mix live code with narrative text and graphics. Jupyter Notebooks are also a great way to share your results with others, because you can save the output of your code sections in the document. You can install Jupyter Notebooks on a variety of platforms.
 
-The procedure in the [Local computer](#local) section installs necessary components for running Jupyter Notebooks in an Anaconda environment. To enable these components in your Jupyter Notebook environment, do the following:
+The procedure in the [Local computer](#local) section installs necessary components for running Jupyter Notebooks in an Anaconda environment.
+
+To enable these components in your Jupyter Notebook environment:
 
 1. Open an Anaconda prompt and activate your environment.
 
     ```shell
     conda activate myenv
     ```
-    
+
 1. Clone [the GitHub repository](https://aka.ms/aml-notebooks) for a set of sample notebooks.
 
     ```CLI
@@ -249,34 +251,35 @@ The procedure in the [Local computer](#local) section installs necessary compone
     import sys
     sys.path
     ```
-    
+
 1. To configure the Jupyter Notebook to use your Azure Machine Learning workspace, go to the [Create a workspace configuration file](#workspace) section.
 
 
 ### <a id="vscode"></a>Visual Studio Code
 
-Visual Studio Code is a cross platform code editor. It relies on a local Python 3 and Conda installation for Python support, but it provides additional tools for working with AI. It also provides support for selecting the Conda environment from within the code editor.
+Visual Studio Code is a very popular cross platform code editor that supports an extensive set of programming languages and tools through extensions available in the [Visual Studio marketplace](https://marketplace.visualstudio.com/vscode). The [Azure Machine Learning extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai) installs the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) for coding in all types of Python environments (virtual, Anaconda, etc.). In addition, it provides convenience features for working with Azure Machine Learning resources and running Azure Machine Learning experiments all without leaving Visual Studio Code.
+
+To use Visual Studio Code for development:
+
+1. Install the Azure Machine Learning extension for Visual Studio Code, see [Azure Machine Learning](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai).
 
-To use Visual Studio Code for development, do the following:
+    For more information, see [Use Azure Machine Learning for Visual Studio Code](how-to-vscode-tools.md).
 
-1. To learn how to use Visual Studio Code for Python development, see [Get started with Python in VSCode](https://code.visualstudio.com/docs/python/python-tutorial).
+1. Learn how to use Visual Studio Code for any type of Python development, see [Get started with Python in VSCode](https://code.visualstudio.com/docs/python/python-tutorial).
 
-1. To select the Conda environment, open VS Code, and then select Ctrl+Shift+P (Linux and Windows) or Command+Shift+P (Mac).
-    The __Command Pallet__ opens.
+    - To select the SDK Python environment containing the SDK, open VS Code, and then select Ctrl+Shift+P (Linux and Windows) or Command+Shift+P (Mac).
+        - The __Command Palette__ opens.
 
-1. Enter __Python: Select Interpreter__, and then select the Conda environment.
+    - Enter __Python: Select Interpreter__, and then select the appropriate environment
 
-1. To validate that you can use the SDK, create and then run a new Python file (.py) that contains the following code:
+1. To validate that you can use the SDK, create a new Python file (.py) that contains the following code:
 
     ```python
+    #%%
     import azureml.core
     azureml.core.VERSION
     ```
-
-1. To install the Azure Machine Learning extension for Visual Studio Code, see [Tools for AI](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai).
-
-    For more information, see [Use Azure Machine Learning for Visual Studio Code](how-to-vscode-tools.md).
-
+    Run this code by clicking the "Run cell" CodeLens or simply press shift-enter.
 <a name="aml-databricks"></a>
 
 ## Azure Databricks
@@ -297,7 +300,7 @@ Use these settings:
 | Setting |Applies to| Value |
 |----|---|---|
 | Cluster name |always| yourclustername |
-| Databricks Runtime |always| Any non ML runtime (non ML 4.x, 5.x) |
+| Databricks Runtime |always| Any non-ML runtime (non-ML 4.x, 5.x) |
 | Python version |always| 3 |
 | Workers |always| 2 or higher |
 | Worker node VM types <br>(determines max # of concurrent iterations) |Automated ML<br>only| Memory optimized VM preferred |
@@ -321,7 +324,7 @@ Once the cluster is running, [create a library](https://docs.databricks.com/user
    * Do not select **Attach automatically to all clusters**.
    * Select  **Attach** next to your cluster name.
 
-1. Monitor for errors until status changes to **Attached**, which may take several minutes.  If this step fails, check the following:
+1. Monitor for errors until status changes to **Attached**, which may take several minutes.  If this step fails:
 
    Try restarting your cluster by:
    1. In the left pane, select **Clusters**.
@@ -397,4 +400,4 @@ You can create the configuration file in three ways:
 ## Next steps
 
 - [Train a model](tutorial-train-models-with-aml.md) on Azure Machine Learning with the MNIST dataset
-- View the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) reference
+- View the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) reference

articles/machine-learning/service/how-to-manage-workspace.md

@@ -17,7 +17,7 @@ ms.custom: seodec18
 
 # Create and manage Azure Machine Learning workspaces in the Azure portal
 
-In this article, you'll create, view, and delete [**Azure Machine Learning workspaces**](concept-workspace.md) in the Azure portal for [Azure Machine Learning](overview-what-is-azure-ml.md).  The portal is the easiest way to get started with workspaces but as your needs change or requirements for automation increase you can also create and delete workspaces [using the CLI](reference-azure-machine-learning-cli.md), [with Python code](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) or [via the VS Code extension](how-to-vscode-tools.md#get-started-with-azure-machine-learning).
+In this article, you'll create, view, and delete [**Azure Machine Learning workspaces**](concept-workspace.md) in the Azure portal for [Azure Machine Learning](overview-what-is-azure-ml.md).  The portal is the easiest way to get started with workspaces but as your needs change or requirements for automation increase you can also create and delete workspaces [using the CLI](reference-azure-machine-learning-cli.md), [with Python code](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) or [via the VS Code extension](how-to-vscode-tools.md#get-started-with-azure-machine-learning-for-visual-studio-code).
 
 ## Create a workspace