Deploying and Hosting ML Model APIs with Vercel Serverless

Question

Pulumi · Accepted Answer

When deploying and hosting ML model APIs using Vercel Serverless, the goal is to create an efficient, scalable, and low-maintenance infrastructure that allows your machine learning models to be served as APIs to client applications. Vercel specializes in serverless functions that run in response to events and automatically scale up and down as needed, making it a good choice for such use cases.

In the context of Pulumi, you would typically achieve this by defining the infrastructure as code, which allows you to automate the deployment and ensures the infrastructure is reproducible, versionable, and maintainable.

Here is a step-by-step plan on how you might proceed:

1. **Set up a Vercel Project**: A Vercel project is a container for your serverless functions. You will create a new Vercel Project using Pulumi and specify settings related to deployment, such as the framework if any, build commands, and environment variables.

2. **Add a Vercel Deployment**: You will need to perform a deployment to Vercel by defining your serverless functions. Each function will be responsible for serving a ML model inference path. The deployment configurations might include the reference to Git for your source code, the path prefix, and whether it's a production deployment.

3. **Configure Environment Variables**: If your ML models require API keys, database connections, or other secrets, you will need to securely provide these to your Vercel project through environment variables.

4. **Handle Domain and Routing**: Vercel automatically provides you with a `.vercel.app` domain, but you can also configure custom domains. You'll define the route configurations for your serverless endpoints if needed.

5. **Monitor and Scale**: Although serverless functions scale automatically, monitoring your usage and performance can be important to ensure optimal experience for your users.

Now, let's write a Pulumi program in Python to set this up:

```python
import pulumi
from pulumi_vercel import Deployment, Project

# Create a new Vercel project.
ml_project = Project("ml-model-api-project",
    name="ml-model-apis",
    teamId="your-vercel-team-id",  # Replace with your Vercel team ID.
    framework=None,  # Assuming we're not using a specific framework.
    # Environment variables could include references to model storage, database URIs, or API keys.
    environments=[
        {
            "key": "MODEL_STORAGE_URI",
            "value": pulumi.Output.secret("s3://my-ml-model-bucket/"),  # Replace with your actual model storage URI.
            "targets": ['production']
        },
        {
            "key": "DATABASE_URI",
            "value": pulumi.Output.secret("postgresql://..."),  # Replace with your actual database URI.
            "targets": ['production']
        }
    ],
    serverlessFunctionRegion="sfo1"  # Choose the region closest to your users.
)

# Deploy the serverless functions to Vercel.
ml_deployment = Deployment("ml-model-api-deployment",
    files= { # You would typically have your serverless function code in GitHub.
        "api/inference.py": "your-local-path-to-inference-file/inference.py"
    },
    teamId="your-vercel-team-id",  # Replace with your Vercel team ID.
    projectId=ml_project.id,
    production=True,  # Set this to False if you want a staging deployment.
    environment={
        "MODEL_STORAGE_URI": "s3://my-ml-model-bucket/",  # Replace with your actual model storage URI.
        "DATABASE_URI": "postgresql://..."  # Replace with your actual database URI.
    }
)

# Export the deployment URL. 
pulumi.export("mlModelApiUrl", ml_deployment.url)
```

Ensure that you replace the placeholders such as `"your-local-path-to-inference-file/inference.py"`, `"your-vercel-team-id"`, `"s3://my-ml-model-bucket/"`, and `"postgresql://..."` with your actual serverless function file path, Vercel team ID, model storage URI, and database URI respectively.

When the Pulumi application is deployed, the `ml_project` creates a new Vercel project to house our serverless functions, and `ml_deployment` deploys the serverless functions as part of the project. We've configured the environment variables for secure access to the resources needed by the machine learning models. The deployment URL is then exported so you can access your new serverless ML model API endpoint.

Remember to install the required Pulumi Vercel provider package before running the Pulumi program:

```shell
pip install pulumi_vercel
```

This Pulumi program manages only the Vercel side of things, assuming you have the machine learning model ready to be served in serverless functions. Depending on your model and its requirements, you may need additional resources or configurations, such as databases or storage buckets, which can also be managed in this Pulumi program by adding the respective provider and resource definitions.