Global AI Model Deployment with GCP BackendService.

Question

Pulumi · Accepted Answer

To deploy a global Artificial Intelligence (AI) model on Google Cloud Platform (GCP), we'll need to set up several resources. One of the main resources we'll use is Google Cloud AI Platform's prediction services which allow us to deploy machine learning models globally.

For the backend service aspect, we'll set up a `BackendService`, which is essentially a group of resources that serve traffic for one or more load-balanced services. We need to create a model first and then an endpoint where this model will be deployed. After deploying the model to the endpoint, the backend service can utilize Google Cloud's load balancing to manage the AI workload.

Here's a Pulumi program written in Python that sets up a Google Cloud AI Platform model, deploys it to an endpoint, and configures a backend service:

```python
import pulumi
import pulumi_gcp as gcp

# Configure Google Cloud settings here, such as project and region
project = 'my-gcp-project'
region = 'us-central1'

# Create a Google Cloud AI Platform Model
ai_model = gcp.ml.EngineModel("ai-model",
    description="My global AI model",
    regions=region,
    project=project)

# Create an AI Platform Endpoint
ai_endpoint = gcp.vertex.AiEndpoint("ai-endpoint",
    project=project,
    location=region,
    displayName="My AI Endpoint")

# Deploy the model to the endpoint
# - Note: You would need a trained and saved model to deploy.
# - In a real scenario, fill in `model_resource_name` and `deployed_model_id`
#   with appropriate values from your trained model.
model_deployment = gcp.vertex.EndpointTrafficSplit("model-deployment",
    endpoint=ai_endpoint.name,
    project=project,
    location=region,
    splits={ai_model.name: 100})  # Direct all traffic to this model.

# Define a backend for the BackendService.
# The group field should point to the instance group where the model is served.
backend = gcp.compute.BackendBucketBackendArgs(
    group=pulumi.Output.concat("projects/", project, "/zones/", region, "/instanceGroups/", ai_endpoint.name),
)

# Create a Backend Service
backend_service = gcp.compute.BackendService("backend-service",
    backends=[backend],
    protocol="HTTP",  # Assuming HTTP here, but configure as needed for your use case.
    project=project)

# Export the AI model and backend service information
pulumi.export("ai_model_name", ai_model.name)
pulumi.export("ai_endpoint_name", ai_endpoint.name)
pulumi.export("backend_service_name", backend_service.name)
```

Let's walk through what the program does:

1. First, we configure the project and region variables to match the specific GCP project and the region where you want to deploy the resources.

2. We define an AI model using the `EngineModel` class, specifying a description, the region to deploy the model, and the project it belongs to.

3. Next, we create an AI Endpoint using the `AiEndpoint` class with display information such as the name. The endpoint is a crucial resource where the model will be served.

4. The model is then deployed to the endpoint using the `EndpointTrafficSplit` class, indicating that we want all traffic to go to this model by setting the splits argument to 100%. The traffic split could be adjusted to allow for A/B testing or other use cases.

5. We set up a backend for the `BackendService` by specifying the instance group. This group is linked to the AI endpoint that we just created.

6. The `BackendService` resource is instantiated, grouping the backend resources (i.e., AI endpoints). The protocol is set to HTTP, assuming we will use HTTP for model serving.

7. Lastly, we export the names of the AI model, AI endpoint, and the backend service so we can easily access these details outside of Pulumi, such as in the GCP console or CLI.

With this Pulumi program, you have set up a global AI model deployment on GCP with a backend service for efficient traffic management. Make sure to fill in the `model_resource_name` and `deployed_model_id` with the actual values from your trained model when you use this in a real-world deployment.