Secure ML Model Serving with GCP VPC

Question

Pulumi · Accepted Answer

To securely serve a machine learning (ML) model with Google Cloud Platform (GCP), we'll create a Virtual Private Cloud (VPC) that provides us a private network on GCP. We'll introduce a few components in this setup:

1. **Google Cloud VPC (Virtual Private Cloud):** Create an isolated network where we can deploy resources. VPC provides a logically isolated network partition on Google Cloud.

2. **Subnet:** Subnets are partitions of a VPC that define the IP address range of the resources in the region.

3. **Google Compute Engine**: Virtual machines that will serve our ML model. We will ensure that these instances are part of the VPC and have access to the ML model.

4. **AI Platform Model Serving**: Google AI Platform allows us to deploy ML models in a managed environment.

5. **Service Networking Connection**: To manage services and service producers' ability to access the VPC, we will create a service networking connection.

6. **IAM Policies**: To define who or what can perform operations on the resources.

Let's illustrate this setup with a Pulumi Python program. We'll deploy a simple ML model on AI Platform and ensure it's accessible from Compute Engine instances within a secure VPC.

```python
import pulumi
import pulumi_gcp as gcp

# 1. Create a GCP VPC.
vpc = gcp.compute.Network("ml-vpc",
    auto_create_subnetworks=False,
    description="VPC for ML Model Serving"
)

# 2. Create a subnet within the VPC in a specific region.
subnet = gcp.compute.Subnetwork("ml-subnet",
    ip_cidr_range="10.0.0.0/16",
    region="us-central1",
    network=vpc.id,
    private_ip_google_access=True
)

# 3. Deploy a Compute Engine VM instance in the subnet we created.
# This instance could host our application that interacts with the ML model.
vm_instance = gcp.compute.Instance("ml-vm",
    machine_type="n1-standard-1",
    zone="us-central1-a",
    boot_disk=gcp.compute.InstanceBootDiskArgs(
        initialize_params=gcp.compute.InstanceBootDiskInitializeParamsArgs(
            image="debian-cloud/debian-9"
        )
    ),
    network_interfaces=[
        gcp.compute.InstanceNetworkInterfaceArgs(
            network=vpc.id,
            subnetwork=subnet.id
        )
    ],
)

# 4. Deploy an ML model to AI Platform for serving.
model = gcp.ml.EngineModel("ml-model",
    # Assuming the model already exists in your GCP storage bucket
    description="My secure ML model deployment",
)

# 5. Create a service networking connection to allow services to access the VPC.
service_networking_connection = gcp.servicenetworking.Connection("ml-model-connection",
    network=vpc.id,
    service="servicenetworking.googleapis.com",
    reserved_peering_ranges=[subnet.secondary_ip_range]
)

# 6. Define IAM policies for access control.
# Define the necessary roles and members for accessing the ML Model.

# Export the IP of the Compute Engine instance to be able to SSH into it later.
pulumi.export("vm_instance_ip", vm_instance.network_interfaces[0].network_ip)
```

In this program:

- We started by creating a `Network` which represents a VPC within GCP, providing us with an isolated network environment. The `auto_create_subnetworks` is set to `False` because we want to have granular control over our subnet configuration.

- We then define a `Subnetwork` within the VPC with a specified IP range where our resources will reside.

- We deploy a `Instance` which is a Google Compute Engine virtual machine that resides within the VPC subnet we created. This instance could be configured to serve as the host for an application that uses the ML model.

- The `EngineModel` is a Pulumi resource for deploying an ML model to GCP's AI Platform. The AI Platform will handle serving the model, and we can configure it to only allow access within our VPC.

- The `Connection` resource represents a Service Networking Connection that allows services to privately access our VPC network. This setup is commonly needed for managed services on GCP that need to interact with resources within a VPC.

- Lastly, we have a placeholder `IAM policies` section, which would involve assigning the correct IAM roles and members for controlling access to the ML model.

The exported IP address of the Compute Engine instance will be useful to connect to the instance, typically via SSH, for maintenance or application deployment.

Remember, this is a high-level guide to get you started, and actual deployment might require additional configuration. Always ensure your security and network settings comply with your organization's policies and standards.