acrolinx

Author: Blackmist

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

@@ -15,23 +15,23 @@ monikerRange: 'azureml-api-2 || azureml-api-1'
 ---
 
 
-# What is an Azure Machine Learning hub workspace? (preview)
+# What is an Azure Machine Learning hub workspace? (Preview)
 
-A hub is a kind of workspace that can be used by IT security to centrally manage security, connectivity, compute resources and quota for a team. Once setup, a hub enables developers to create their own workspaces to organize their work while staying compliant with IT setup requirements. Sharing and reuse of configurations through a hub workspace yields better cost efficiency when deploying Azure Machine Learning at scale.
+A hub is a kind of workspace that centrally manages security, connectivity, compute resources, and quota for a team. Once set up, a hub enables developers to create their own workspaces to organize their work while staying compliant with IT setup requirements. Sharing and reuse of configurations through a hub workspace yields better cost efficiency when deploying Azure Machine Learning at scale.
 
-Workspaces that are created using a hub, referred to as 'project workspaces', obtain the same security settings and shared resource access. They don't require their own security settings or Azure [associated resources](concept-workspace.md#associated-resources). Create as many project workspaces as you need to organize your work, isolate data, or restrict access. 
+Workspaces that are created using a hub, referred to as 'project workspaces,' obtain the same security settings and shared resource access. They don't require their own security settings or Azure [associated resources](concept-workspace.md#associated-resources). Create as many project workspaces as you need to organize your work, isolate data, or restrict access. 
 
 Create a hub workspace if you or your team are planning for multiple machine learning projects. Use a hub to organize your work in the same data or business domain.
 
 ![Diagram showing hub and project workspace relation](media/concept-hub-workspace/hub-workspace-relation.png)
 
 ## Fast, but secure, AI exploration without bottleneck on IT
 
-Successfully building machine learning models often requires heavy prototyping as steppingstone for a full-scale implementation. It may be embodied to prove the feasibility of an idea, assess quality of data or a model, for a particular task.
+Successfully building machine learning models often requires heavy prototyping as prerequisite for a full-scale implementation. It might be embodied to prove the feasibility of an idea, or assess quality of data or a model, for a particular task.
 
-In the transition from proving feasibility of an idea, to a funded project, many organizations encounter a bottleneck in productivity because a single platform team is responsible for the setup of cloud resources. Such a team may be the only one authorized to configure security, connectivity or other resources that may incur costs. This may cause a huge backlog, resulting in development teams getting blocked to start innovating with a new idea.
+In the transition from proving feasibility of an idea, to a funded project, many organizations encounter a bottleneck in productivity because a single platform team is responsible for the setup of cloud resources. Such a team might be the only one authorized to configure security, connectivity or other resources that might incur costs. This might cause a huge backlog, resulting in development teams getting blocked to start innovating with a new idea.
 
-The goal of hubs is to take away this bottleneck, by letting IT set up a secure, pre-configured and reusable environment for a team to prototype, build and operate machine learning models.
+The goal of hubs is to take away this bottleneck, by letting IT set up a secure, preconfigured, and reusable environment for a team to prototype, build, and operate machine learning models.
 
 ## Interoperability between ML studio and AI studio
 
@@ -45,24 +45,24 @@ Hubs can be used as your team's collaboration environment for both ML studio and
 
 ## Set up and secure a hub for your team
 
-Create a hub workspace in [Azure Portal](how-to-manage-hub-workspace-portal.md), or using [Azure Resource Manager templates](how-to-manage-hub-workspace-template.md). You may customize networking, identity, encryption, monitoring or tags, to meet compliance with your organization’s requirements. 
+Create a hub workspace in [Azure portal](how-to-manage-hub-workspace-portal.md), or using [Azure Resource Manager templates](how-to-manage-hub-workspace-template.md). You might customize networking, identity, encryption, monitoring, or tags, to meet compliance with your organization's requirements. 
 
-Project workspaces that are created using a hub, obtain the hub’s security settings and shared resource configuration. This includes the following configurations:
+Project workspaces that are created using a hub obtain the hub's security settings and shared resource configuration. Including the following configurations:
 
 | Configuration | Note |
 | ---- | ---- |
-| Network settings | One [managed virtual network](how-to-managed-network.md) is shared between hub and project workspaces. Create a single private link endpoint on the hub workspace, to access content in hub and project workspaces. |
+| Network settings | One [managed virtual network](how-to-managed-network.md) is shared between hub and project workspaces. To access content in the hub and project workspaces, create a single private link endpoint on the hub workspace. |
 | Encryption settings | Encryption settings pass down from hub to project. |
-| Storage for encrypted data | When bringing your customer-managed keys for encryption, hub and project workspaces share the same [managed resource group]() for storing encrypted service data. |
+| Storage for encrypted data | When you bring your customer-managed keys for encryption, hub and project workspaces share the same managed resource group for storing encrypted service data. |
 | Connections | Project workspaces can consume shared connections created on the hub. This feature is currently only supported in [AI studio]()  |
 | Compute instance | Reuse a compute instance across all project workspaces associated to the same hub. |
-| Compute quota | Any compute quota consumed by project workspaces are deducted from the hub workspace quota balance. |
-| Storage | Associated resource for storing workspace data. Project workspaces have designated containers starting with a prefix {workspaceGUID}, and have a conditional [Azure Attribute Based Access](../role-based-access-control/conditions-overview.md) role assignment for the workspace identity for accessing these containers only. |
-| Key vault | Associated resource for storing secrets created in the service e.g. when creating a connection. Project workspaces identities can only access their own secrets. |
+| Compute quota | Any compute quota consumed by project workspaces is deducted from the hub workspace quota balance. |
+| Storage | Associated resource for storing workspace data. Project workspaces use designated containers starting with a prefix {workspaceGUID}, and have a conditional [Azure Attribute Based Access](../role-based-access-control/conditions-overview.md) role assignment for the workspace identity for accessing these containers only. |
+| Key vault | Associated resource for storing secrets created in the service, for example, when creating a connection. Project workspaces identities can only access their own secrets. |
 | Container registry | Associated resource for storing built container images when creating environments. Project workspaces images are isolated by naming convention, and can only access their own containers. |
-| Application insights | Associated resource when enabling application logging for endpoints. One application insights may be configured as default for all project workspaces. Can be overridden on project workspace-level. |
+| Application insights | Associated resource when enabling application logging for endpoints. One application insights might be configured as default for all project workspaces. Can be overridden on project workspace-level. |
 
-Data that is uploaded in one project workspace, is stored in isolation from data that is uploaded to another project workspace. While project workspaces reuse hub security settings, they are still top-level Azure resources, which enable you to restrict access to only project members.
+Data that is uploaded in one project workspace, is stored in isolation from data that is uploaded to another project workspace. While project workspaces reuse hub security settings, they're still top-level Azure resources, which enable you to restrict access to only project members.
 
 ## Create a project workspace using a hub
 
@@ -82,9 +82,9 @@ Once a hub is created, there are multiple ways to create a project workspace usi
 
 To create project workspaces using a hub, users must have a role assignment on the hub workspace resource using a role that includes the **Microsoft.MachineLearningServices/workspaces/hubs/join/action** action. Azure AI developer role is an example built-in role that supports this action.
 
-Optionally, when creating a hub as an administrator, you may specify a default project resource group to allow users to create project workspaces in a self-service manner. If this default resource group is set, users who use the SDK/CLI/Studio experience can create workspaces in this particular resource group without needing further Azure RBAC permissions on a resource group-scope. The creating user, will become an owner on the project workspace Azure resource. 
+Optionally, when creating a hub as an administrator, you might specify a default project resource group to allow users to create project workspaces in a self-service manner. If a default resource group is set, SDK/CLI/Studio users can create workspaces in this resource group without needing further Azure role-based access control (Azure RBAC) permissions on a resource group-scope. The creating user becomes an owner on the project workspace Azure resource. 
 
-Project workspaces can be created in other resource groups than the default project resource group. For this, users need Microsoft.MachineLearning/Workspaces/write permissions.
+Project workspaces can be created in other resource groups than the default project resource group. To do so, users need Microsoft.MachineLearning/Workspaces/write permissions.
 
 ## Supported capabilities by workspace kind
 
@@ -95,7 +95,7 @@ Features that are supported using hub/project workspaces differ from regular wor
 |Self-serve create project workspaces from Studio| - | X | X | - |
 |Create shared connections on hub | |X|X| Only in AI studio |
 |Consume shared connections from hub | |X|X| - |
-|Reuse compute instance across workspaces|-|X|X|
+|Reuse compute instance across workspaces|-|X|X| |
 |Share compute quota across workspaces|-|X|X||
 |Build GenAI apps in AI studio|-|X|X||
 |Single private link endpoint across workspaces|-|X|X||
@@ -106,7 +106,7 @@ Features that are supported using hub/project workspaces differ from regular wor
 
 ## Converting a regular workspace into a hub workspace
 
-This is currently not supported.
+Not supported.
 
 ## Next steps