Create Resource button changed to execute yaml

Author: ssalgadodev

articles/machine-learning/how-to-manage-resources-vscode.md

@@ -32,7 +32,7 @@ The quickest way to create resources is using the extension's toolbar.
 1. Select **+** in the activity bar.
 1. Choose your resource from the dropdown list.
 1. Configure the specification file. The information required depends on the type of resource you want to create.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, you can create a resource by using the command palette:
 
@@ -50,7 +50,7 @@ To version a resource:
 
 1. Use the existing specification file that created the resource or follow the create resources process to create a new specification file.
 1. Increment the version number in the template.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 As long as the name of the updated resource is the same as the previous version, Azure Machine Learning picks up the changes and creates a new version.
 
@@ -62,7 +62,7 @@ For more information, see [workspaces](concept-workspace.md).
 
 1. In the Azure Machine Learning view, right-click your subscription node and select **Create Workspace**.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Workspace` command in the command palette.
 
@@ -94,7 +94,7 @@ For more information, see [datastores](concept-data.md#datastores).
 1. Right-click the **Datastores** node and select **Create Datastore**.
 1. Choose the datastore type.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Datastore` command in the command palette.
 
@@ -124,7 +124,7 @@ For more information, see [datasets](concept-data.md#datasets)
 1. Expand the workspace node you want to create the dataset under.
 1. Right-click the **Datasets** node and select **Create Dataset**.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Dataset` command in the command palette.
 
@@ -150,7 +150,7 @@ For more information, see [environments](concept-environments.md).
 1. Expand the workspace node you want to create the datastore under.
 1. Right-click the **Environments** node and select **Create Environment**.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Environment` command in the command palette.
 
@@ -180,7 +180,7 @@ Using the resource nodes in the Azure Machine Learning view:
 1. Right-click the **Experiments** node in your workspace and select **Create Job**.
 1. Choose your job type.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Job` command in the command palette.
 
@@ -229,7 +229,7 @@ For more information, see [compute instances](concept-compute-instance.md).
 1. Expand the **Compute** node.
 1. Right-click the **Compute instances** node in your workspace and select **Create Compute**.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Compute` command in the command palette.
 
@@ -275,7 +275,7 @@ For more information, see [training compute targets](concept-compute-target.md#t
 1. Expand the **Compute** node.
 1. Right-click the **Compute clusters** node in your workspace and select **Create Compute**.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Compute` command in the command palette.
 
@@ -346,7 +346,7 @@ For more information, see [models](concept-azure-machine-learning-architecture.m
 1. Expand your workspace node.
 1. Right-click the **Models** node in your workspace and select **Create Model**.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Model` command in the command palette.
 
@@ -386,7 +386,7 @@ For more information, see [endpoints](concept-azure-machine-learning-architectur
 1. Right-click the **Models** node in your workspace and select **Create Endpoint**.
 1. Choose your endpoint type.
 1. A specification file appears. Configure the specification file.
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 Alternatively, use the `> Azure ML: Create Endpoint` command in the command palette.
 

articles/machine-learning/tutorial-train-deploy-image-classification-model-vscode.md

@@ -71,7 +71,7 @@ The first thing you have to do to build an application in Azure Machine Learning
 
     The specification file creates a workspace called `TeamWorkspace` in the `WestUS2` region. The rest of the options defined in the specification file provide friendly naming, descriptions, and tags for the workspace.
 
-1. Right-click the specification file and select **Azure ML: Create Resource**. Creating a resource uses the configuration options defined in the YAML specification file and submits a job using the CLI (v2). At this point, a request to Azure is made to create a new workspace and dependent resources in your account. After a few minutes, the new workspace appears in your subscription node.
+1. Right-click the specification file and select **Azure ML: Execute YAML**. Creating a resource uses the configuration options defined in the YAML specification file and submits a job using the CLI (v2). At this point, a request to Azure is made to create a new workspace and dependent resources in your account. After a few minutes, the new workspace appears in your subscription node.
 1. Set `TeamWorkspace` as your default workspace. Doing so places resources and jobs you create in the workspace by default. Select the **Set Azure ML Workspace** button on the Visual Studio Code status bar and follow the prompts to set `TeamWorkspace` as your default workspace.
 
 For more information on workspaces, see [how to manage resources in VS Code](how-to-manage-resources-vscode.md).
@@ -103,7 +103,7 @@ A compute target is the computing resource or environment where you run training
 
     For more information on VM sizes, see [sizes for Linux virtual machines in Azure](../virtual-machines/sizes.md).
 
-1. Right-click the specification file and select **Azure ML: Create Resource**.
+1. Right-click the specification file and select **Azure ML: Execute YAML**.
 
 After a few minutes, the new compute target appears in the *Compute > Compute clusters* node of your workspace.
 
@@ -131,10 +131,7 @@ This specification file submits a training job called `tensorflow-mnist-example`
 To submit the training job:
 
 1. Open the *job.yml* file.
-1. Right-click the file in the text editor and select **Azure ML: Create Resource**.
-
-> [!div class="mx-imgBorder"]
-> ![Run experiment](./media/tutorial-train-deploy-image-classification-model-vscode/run-experiment.png)
+1. Right-click the file in the text editor and select **Azure ML: Execute YAML**.
 
 At this point, a request is sent to Azure to run your experiment on the selected compute target in your workspace. This process takes several minutes. The amount of time to run the training job is impacted by several factors like the compute type and training data size. To track the progress of your experiment, right-click the current run node and select **View Run in Azure portal**.