Integrating ML Model Serving with GCP Workflows

Question

Pulumi · Accepted Answer

To integrate ML model serving with GCP Workflows using Pulumi, we will need to leverage several Google Cloud services:

1. **Google Cloud Machine Learning (ML) Engine**: We'll use this to host our trained ML model. This service allows for easy deployment of machine learning models in a serverless environment.
   
2. **Google Cloud Workflows**: This fully managed service orchestrates and automates Google Cloud and HTTP-based API services with serverless workflows.

3. **Pulumi**: We'll use Pulumi to define, deploy, and manage our infrastructure as code. Pulumi's Google Cloud provider (called `pulumi_gcp`) allows you to interact with these services programmatically.

In the following Pulumi program, we will:

- Create a Machine Learning Model using ML Engine.
- Set up a Workflow to trigger inference requests and process them.

### Prerequisites:
- A Google Cloud account and a Project.
- Pulumi [CLI](https://www.pulumi.com/docs/get-started/install/) installed and configured for GCP.
- Access to the trained ML model that you want to deploy.

Let's start by writing our Pulumi program. Note that in a real-world scenario, you would need a trained model ready for deployment, which is beyond the scope of this example. We will hence focus on how to set up the necessary infrastructure.

```python
import pulumi
import pulumi_gcp as gcp

# Replace these variables with your own information
project_id = "your-project-id"
region = "your-region"
model_name = "your-model-name"
model_description = "Description of your model"

# Create a ML Model resource
ml_model = gcp.ml.Model("ml-model",
    project=project_id,
    description=model_description,
    name=model_name,
    regions=[region],
    online_prediction_logging=True,
    online_prediction_console_logging=True
)

# Define the Workflow that serves the ML model
# In the real-world, you would also add steps to pre-process inputs, post-process outputs,
# handle errors, etc.
workflow_yaml = f"""
main:
  params: [args]
  steps:
  - init:
      assign:
      - project: "{project_id}"
      - model: "{model_name}"
      - payload: ${{args}}
  - predict:
      call: http.post
      args:
        url: https://ml.googleapis.com/v1/projects/${{project}}/models/${{model}}:predict
        body:
          instances: [${{payload}}]
        auth:
          type: OAuth2
      result: prediction
"""

workflow = gcp.workflows.Workflow("ml-serving-workflow",
    project=project_id,
    region=region,
    description="A workflow to serve ML predictions",
    source_contents=workflow_yaml
)

pulumi.export("model_name", ml_model.name)
pulumi.export("workflow_name", workflow.name)
```

The code starts by importing the required Pulumi modules and defining some variables that you should replace with actual values from your GCP Project and ML model information.

It then creates a `Model` resource that configures the deployment of your ML model on Google Cloud ML Engine. The `regions` parameter specifies the GCP region where you'd like to host your model. Online prediction logging options are turned on for auditing purposes.

Next, we define the workflow with YAML formatted as a multi-line string. This workflow has two steps:
- `init`: Prepares the payload and other variables.
- `predict`: Makes an HTTP POST request to the ML Engine's prediction service using the payload as the body. The `auth` field specifies OAuth2 authentication which is required for Google services.

The `Workflow` resource represents our serverless workflow that includes our YAML definition.

Finally, we export the names of the created model and workflow so you can easily reference them later, for example when testing or updating your infrastructure.

Remember to replace the `project_id`, `region`, `model_name`, and `model_description` variables with your own details. Also, consider enhancing error handling and securing sensitive data using Pulumi's configuration system when adapting this for a production environment.