Organizing Azure AI Services by Project.

Question

Pulumi · Accepted Answer

When organizing Azure AI Services by project, you generally look into grouping related resources together logically so that they can be managed as a single unit. This can involve creating specific resource groups for each project, employing naming conventions for resources that reflect their purpose and their association with a particular project, and taking advantage of Azure's services that are designed to handle project-based organization, such as the Azure Machine Learning Workspace.

Pulumi enables you to define, deploy, and manage Azure resources in a structured and repeatable way using Infrastructure as Code (IaC). This empowers you to version control your infrastructure, review changes before they are applied, and automate your deployments.

Below is a Pulumi program written in Python that demonstrates how to organize Azure AI services by project. In this program, we will create an Azure Machine Learning Workspace with associated resources like an Experimentation Account and Projects to group various machine learning experiments.

In the provided example, we are making use of the following Azure resources:

- **Resource Group**: Acts as a container that holds related resources for an Azure solution. Here, we will create a resource group for our AI services.
- **Machine Learning Workspace**: This is an Azure Machine Learning resource that provides a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace holds all your experiments, machine learning pipelines, and deployments.
- **Machine Learning Experimentation Account**: This account provides access to the experimentation services. You can manage projects under this account.
- **Machine Learning Experimentation Project**: Within the account, projects are logical containers that allow you to organize your experiments into separate groups.

The following Python program assumes that you've already set up Pulumi and authenticated with Azure:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a resource group for AI services
resource_group = azure_native.resources.ResourceGroup("ai_project_resource_group")

# Create an Azure Machine Learning Workspace in the resource group
ml_workspace = azure_native.machinelearningservices.Workspace(
    "machine_learning_workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # Further Workspace settings can be configured here as necessary
)

# Create an Experimentation Account (for demonstration, actual configuration may vary)
ml_experimentation_account = azure_native.machinelearningexperimentation.Account(
    "experimentation_account",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # Further Account settings can be configured here as necessary
)

# Create an Experimentation Project within the Experimentation Account
ml_experimentation_project = azure_native.machinelearningexperimentation.Project(
    "experimentation_project",
    account_name=ml_experimentation_account.name,
    resource_group_name=resource_group.name,
    # Further Project settings can be configured here as necessary
)

# Output the resource group name and workspace URL
pulumi.export("resource_group_name", resource_group.name)
pulumi.export("ml_workspace_url", ml_workspace.workspace_url)
```

The above program will create an organization structure for AI services in Azure, where each service is related to a project inside an experimentation account, and everything is bundled under an Azure Machine Learning Workspace within a resource group. The final lines of the program export the name of the resource group and the URL of the Machine Learning Workspace, which can be useful for accessing the workspace through the Azure portal or for automation scripts that need to reference these resources.

Remember to replace placeholders (like the names of resources) with actual values that suit your project. Additionally, you should consider adding tags and other descriptive properties to resources to better organize and categorize them within Azure.