positioning workspace as a per-project entity.

Author: Larry Franks

articles/machine-learning/concept-workspace.md

@@ -17,9 +17,12 @@ ms.date: 08/26/2022
 
 # What is an Azure Machine Learning workspace?
 
-The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning.  The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model.  
+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.
 
-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.
+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).
 
 ## Taxonomy 
 
@@ -103,7 +106,7 @@ When you create a new workspace, it automatically creates several Azure resource
   > By default, the storage account is a general-purpose v1 account. You can [upgrade this to general-purpose v2](../storage/common/storage-account-upgrade.md) after the workspace has been created. 
   > Do not enable hierarchical namespace on the storage account after upgrading to general-purpose v2.
 
-  To use an existing Azure Storage account, it cannot be of type BlobStorage or a premium account (Premium_LRS and Premium_GRS). It also cannot have a hierarchical namespace (used with Azure Data Lake Storage Gen2). Neither premium storage nor hierarchical namespaces are supported with the _default_ storage account of the workspace. You can use premium storage or hierarchical namespace with _non-default_ storage accounts.
+  To use an existing Azure Storage account, it can't be of type BlobStorage or a premium account (Premium_LRS and Premium_GRS). It also can't have a hierarchical namespace (used with Azure Data Lake Storage Gen2). Neither premium storage nor hierarchical namespaces are supported with the _default_ storage account of the workspace. You can use premium storage or hierarchical namespace with _non-default_ storage accounts.
 
 + [Azure Container Registry](https://azure.microsoft.com/services/container-registry/): Registers docker containers that are used for the following components:
     * [Azure Machine Learning environments](concept-environments.md) when training and deploying models