incorporating feedback

Author: Larry Franks

articles/machine-learning/concept-workspace.md

@@ -10,19 +10,32 @@ ms.topic: conceptual
 ms.author: deeikele
 author: deeikele
 ms.reviewer: sgilley
-ms.date: 08/26/2022
+ms.date: 03/13/2023
 #Customer intent: As a data scientist, I want to understand the purpose of a workspace for Azure Machine Learning.
 ---
 
 
 # What is an Azure Machine Learning workspace?
 
-Workspaces are places to collaborate with colleagues and group related work. For example, experiments, jobs, datasets, components, and inference endpoints. The workspace also keeps a history of all training jobs, 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 an [online endpoint](concept-endpoints.md) as a REST-based HTTP endpoint.
+Workspaces are places to collaborate with colleagues and group related work. For example, experiments, jobs, datasets, components, and inference endpoints. 
+
+:::image type="content" source="./media/concept-workspace/workspace.png" alt-text="Screenshot of the Azure Machine Learning workspace.":::
 
 We recommend creating a workspace _per project_. While a workspace can be used for multiple projects, limiting it to one project per workspace allows for cost reporting accrued to a project level. It also allows you to manage configurations like datastores in the scope of each project.
 
-> [!TIP]
-> For collaboration between workspaces, consider [Azure Machine Learning registries (preview)](how-to-share-models-pipelines-across-workspaces-with-registries.md).
+
+## Working with a workspace
+
+Machine learning tasks read and/or write artifacts to your 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 jobs, pipelines, models, deployments.
++ Track and monitor models.
++ You can share assets between workspaces using [Azure Machine Learning registries (preview)](how-to-share-models-pipelines-across-workspaces-with-registries.md).
 
 ## Taxonomy 
 
@@ -48,18 +61,6 @@ You can interact with your workspace in the following ways:
 + 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 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 jobs, pipelines, models, deployments.
-+ Track and monitor models.
-
 ## Workspace management
 
 You can also perform the following workspace management tasks: