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

Co-authored-by: Larry Franks <[email protected]>

Author: deeikele

articles/machine-learning/concept-workspace.md

@@ -29,7 +29,7 @@ Ready to get started? [Create a workspace](#create-a-workspace).
 For machine learning teams, the workspace is a place to organize their work. To administrators, workspaces serve as containers for access management, cost management and data isolation. Below are some tips for working with workspaces:
 
 + **Use [user roles](how-to-assign-roles.md)** for permission management in the workspace between users. For example a data scientist, a machine learning engineer or an admin.
-+ **Assign access to user groups**: By using Azure Active Directory user groups you don't have to add individual users to each workspace and other resources the same group of users requires access to.
++ **Assign access to user groups**: By using Azure Active Directory user groups, you don't have to add individual users to each workspace and other resources the same group of users requires access to.
 + **Create 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.
 + **Share Azure resources**: Workspaces require you to create a number of [associated resources](#associated-resources). Share these resources between workspaces to save repetitive set up steps.
 + **Enable self-serve**: Pre-create and secure [associated resources](#associated-resources) as an IT admin, and use [user roles](how-to-assign-roles.md) to let data scientists create workspaces on their own.