Granting Access to Team Members for Machine Learning Workspace Resources
Question
Machine Learning workspace is the root object for running ML experiments in Azure.
You have created one so that you can train your models, run auto ML experiments, build reusable workflows, evaluate models etc.
You have to grant access to a number of team members to resources in your workspace.
Which tools can you use to complete this task?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option A is incorrect because neither ML studio nor the SDKs have workspace management features.
Option B is incorrect becausethe SDKs don't have workspace management features.
Option C is incorrect because neither ML studio nor the SDKs have workspace management features.
Option D is CORRECT because for your access management tasks you have to use the Portal or Azure CLI.
These are the tools Azure provides for access management.
Reference:
To grant access to team members to the resources in your Machine Learning workspace in Azure, you can use the following tools:
D. Azure Portal + Azure CLI.
The Azure Portal is a web-based interface that allows you to manage and configure Azure resources. You can use the portal to create, modify, and manage resources such as virtual machines, databases, and storage accounts.
Azure CLI is a command-line tool that allows you to manage Azure resources from the command line or scripts. You can use the Azure CLI to create and manage Azure resources, automate deployment, and configure security.
To grant access to your team members to your Machine Learning workspace, you can use the Azure Portal to add them as users to your workspace. You can grant them access to specific resources within the workspace, such as experiments, models, or datasets.
Alternatively, you can use Azure CLI to add users to your workspace using the az ml workspace share command. This command allows you to grant access to specific users or groups with specific roles, such as Contributor or Reader.
In conclusion, you can use the Azure Portal and Azure CLI to grant access to team members to the resources in your Machine Learning workspace in Azure. Both tools offer different approaches to managing Azure resources, so you can choose the one that best fits your workflow and preferences.
