Configuring Public IPs for Access to Azure Machine Learning Studio Endpoints

Question

Pulumi · Accepted Answer

To configure public IPs for accessing Azure Machine Learning Studio endpoints, we need to set up an Azure Machine Learning Workspace and then we can assign public IP addresses to allow external access to the endpoints within the workspace. This typically involves creating an Azure Machine Learning workspace, and then a compute instance within that workspace, which will serve your trained models as endpoints. Here's how you can do it using Pulumi:

1. **Provision an Azure Machine Learning Workspace**: The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning.

2. **Public IP Address**: We will assign a public IP address to your compute resources to make the endpoints accessible over the internet. Be cautious with this as it will expose your services to the internet.

3. **Network Security Groups (NSGs)**: These are used to control inbound and outbound traffic to network interfaces (NIC), VMs, and subnets. Each NSG contains a list of access control list (ACL) rules that allow or deny network traffic to your VM instances in a Virtual Network.

4. **Compute Instances**: They are used to run your machine learning models. These instances can be associated with public IP addresses to expose the endpoints.

Let's proceed to write a Pulumi program in Python to set this up. In the following program, we assume that you have the Azure Machine Learning workspace and necessary network resources already provisioned. We will focus on the steps to configure public IP addresses and ensure that the endpoints are accessible:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup('resource_group')

# Provision a public IP address in the Resource Group
public_ip = azure_native.network.PublicIPAddress(
    "publicIp",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    public_ip_allocation_method=azure_native.network.IPAllocationMethod.STATIC,
    sku=azure_native.network.PublicIPAddressSkuArgs(
        name=azure_native.network.PublicIPAddressSkuName.STANDARD,
    )
)

# Create a Network Security Group to allow traffic on port 80 and 443
nsg = azure_native.network.NetworkSecurityGroup(
    "nsg",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    security_rules=[
        azure_native.network.SecurityRuleArgs(
            name="AllowHTTP",
            protocol=azure_native.network.SecurityRuleProtocol.TCP,
            source_address_prefix="*",
            destination_address_prefix="*",
            access=azure_native.network.SecurityRuleAccess.ALLOW,
            direction=azure_native.network.SecurityRuleDirection.INBOUND,
            priority=100,
            source_port_range="*",
            destination_port_range="80",
        ),
        azure_native.network.SecurityRuleArgs(
            name="AllowHTTPS",
            protocol=azure_native.network.SecurityRuleProtocol.TCP,
            source_address_prefix="*",
            destination_address_prefix="*",
            access=azure_native.network.SecurityRuleAccess.ALLOW,
            direction=azure_native.network.SecurityRuleDirection.INBOUND,
            priority=110,
            source_port_range="*",
            destination_port_range="443",
        ),
    ]
)

# Output the public IP address
pulumi.export('public_ip_address', public_ip.ip_address)
```

In this program, we start by creating a resource group in Azure to hold our resources.

Next, we create a static `PublicIPAddress` which could be associated with your compute instance within Azure Machine Learning or any other relevant service you've set up.

After that, we establish network security groups to define the rules that allow traffic through the HTTP and HTTPS ports, which are usually used for web services.

Lastly, we export the IP address so that you can reference it outside of Pulumi.

Remember to replace placeholders and add additional configurations as needed, such as associating this public IP address with your specific compute instance in Azure Machine Learning. Ensure that the compute instances are set up within the specified subnet and that the correct network interface configurations are in place to associate the public IP address.

```python
# It is important to associate the public IP with a Network Interface or any other Azure resource allowing associating public IPs.
```

Please ensure that you are following Azure's security best practices when exposing services with public IPs. It's generally a good practice to minimize the exposure of endpoints to the public internet and to make use of Azure's security features to protect your services.