Securing AI Model Serving Endpoints with AWS ACM

Question

Pulumi · Accepted Answer

To secure AI model serving endpoints with AWS Certificate Manager (ACM), you need to create and manage an SSL/TLS certificate to enable HTTPS connections. Here's what you need to do:

1. **Request a new certificate**: Use ACM to request a new SSL/TLS certificate for your endpoint's domain. You can validate ownership of the domain using either DNS validation or email validation.
2. **Deploy your model**: Once the SSL/TLS certificate is ready, deploy your AI model on a server. If you are using Amazon SageMaker, for example, it handles HTTPS endpoints for you.
3. **Associate the certificate**: Depending on where your endpoint is hosted, integrate the ACM certificate. For an Amazon API Gateway, you associate the certificate directly. If you're using an Application Load Balancer (ALB) in front of an EC2 instance, you would attach the ACM certificate to the ALB.

Let's walk through the process of requesting a certificate and using it with API Gateway, which is common for serverless API endpoints, using Pulumi.

Below is the Pulumi code to create an SSL/TLS certificate and validate it. The `aws.acm.Certificate` resource is used to request a certificate, and `aws.acm.CertificateValidation` ensures it's validated and can be associated with an endpoint, such as an API Gateway:

```python
import pulumi
import pulumi_aws as aws

# Replace with your domain name
domain_name = "example.com"

# Request a new SSL/TLS certificate for the domain.
certificate = aws.acm.Certificate("myDomainCert",
    domain_name=domain_name,
    validation_method="DNS",
)

# Use this only if you're managing DNS records with AWS Route53.
# It sets up the DNS record to prove ownership of the domain to AWS ACM
dns_record = aws.route53.Record("myDomainValidationRecord",
    zone_id=aws_route53_zone["my_zone"]["id"],
    name=certificate.domain_validation_options[0].resource_record_name,
    type=certificate.domain_validation_options[0].resource_record_type,
    records=[certificate.domain_validation_options[0].resource_record_value],
    ttl=300,
)

# Validate the certificate to confirm domain ownership.
certificate_validation = aws.acm.CertificateValidation("myDomainCertValidation",
    certificate_arn=certificate.arn,
    validation_record_fqdns=[dns_record.fqdn],
)

# Output the certificate ARN
pulumi.export("certificate_arn", certificate_validation.certificate_arn)
```

In the above program:

- We request an SSL/TLS certificate using `aws.acm.Certificate` for a domain and specify the validation method to validate domain ownership.
- A DNS record is created for domain validation if you're using AWS Route53 as your DNS provider. If not, you should manually or otherwise configure the DNS validation record through your DNS provider.
- `aws.acm.CertificateValidation` is used to ensure the certificate is validated.
- Once the certificate is validated, its ARN is exported, which you can use to associate with API Gateway or other services requiring a certificate.

Remember:
- Replace "example.com" with your domain name.
- If you're not using Route53 for DNS, you must set up the domain validation step outside Pulumi.

Next, you would deploy your model to your desired AWS service like Amazon SageMaker, Lambda Function, or an EC2 instance, and make this certificate ARN part of your service configuration to secure your endpoint with HTTPS.

Would you like to proceed with additional steps such as deploying an AI model and exposing it via API Gateway with this certificate? Let me know if you'd like a comprehensive example that covers the full process, and which AWS services you're planning to use.