Welcome to this section, where you will explore Azure
Machine Learning workspaces. A workspace is a context
for the experiments, data, compute targets,
and other assets associated with the
machine learning workload. A workspace defines
the boundary for a set of related machine
learning assets. You can use workspaces to group machine learning
assets based on projects, deployment environments,
for example, test and production, teams, or some other
organizing principle. The assets in a
workspace include; compute targets for development
training and deployment, data for experimentation
and model training, notebooks containing
shared code and documentation, experiments, including run history with
logged metrics and outputs, pipelines that define
orchestrated multi-step processes and models that
you have trained. Workspaces are Azure
resources, and as such, they are defined within
a resource group in an Azure subscription, along with other
related Azure resources that are required to
support the workspace. The Azure resources
created alongside a workspace include;
a storage account, which is used to
store files used by the workspace as well as data for experiments
and model training. An Application
Insights instance, which is used to monitor predictive services
in the workspace. An Azure Key Vault instance, which is used to
manage secrets such as authentication keys
and credentials used by the workspace, and a container registry
which is created as needed to manage containers
for deployed models. You can assign role-based
authorization policies to a workspace
enabling you to manage permissions that
restrict what actions specific Azure Active Directory or AAD principals can perform. For example, you could
create a policy that allows only users in the IT
operations group to create, compute targets and data stores. While allowing users in
the Data Scientists group to create and run experiments
and register models. You can create a workspace
in a number of ways. In the Microsoft Azure Portal, create a new machine
learning resource specifying the subscription
resource group and workspace name. Or you can use the Azure
Machine Learning Python SDK to run code that
creates a workspace. For example, you can create a workspace named aml-workspace, assuming the Azure Machine
Learning SDK for Python is installed and a valid
subscription ID specified. Use the Azure Command
Line Interface or CLI with the Azure Machine
Learning CLI extension. For example, you could use
an aml-workspace command, which assumes a resource group named aml-resources has
already been created. For more information about the template format for an Azure Machine
Learning workspace, see the Azure Machine
Learning documentation from the Microsoft website. You can find a link
to this document from the additional readings at
the end of this lesson.