1. Multi-domain SSL Certificate Handling for AI Model Servers

    Python

    To handle SSL certificates for AI model servers, especially when serving multiple domains, you need to automate the SSL certificate management process. This includes provisioning, deploying, and renewing SSL certificates for each domain to ensure secure HTTPS connections.

    For handling multi-domain SSL certificates, one common method is to use a wildcard certificate that covers multiple subdomains under a single domain. However, for distinct multiple domains, you typically use a SAN (Subject Alternative Name) certificate that can secure several domain names with a single certificate.

    Cloud providers offer different solutions to manage SSL certificates. Here's how you could handle this using Google Cloud Platform (GCP) as an example, with the ManagedSslCertificate resource. ManagedSslCertificate allows you to provision, manage, and deploy SSL certificates provided by Google, which can be used with HTTPS load balancers.

    Here's a Pulumi program that demonstrates how to use GCP's managed SSL certificates for multiple domains.

    First, you would define a managed SSL certificate resource and list all domains you want to secure with the certificate. Then, you would associate this SSL certificate with an HTTPS load balancer to serve your AI model servers over SSL.

    import pulumi import pulumi_gcp as gcp # Assume you have already set up your AI model servers and assigned them to a backend service. # Define a managed SSL certificate for your domains. managed_ssl_certificate = gcp.compute.ManagedSslCertificate("managed-ssl-certificate", name="my-ai-model-servers-ssl", managed=pulumi_gcp.compute.ManagedSslCertificateManagedArgs( domains=["modelserver1.example.com", "modelserver2.example.com"], ) ) # Here, you would typically have an HTTPS target proxy configured that points to # an URL map which directs traffic to your model server backends based on the domains/subdomains. # The SSL certificates would be attached to the HTTPS target proxy. # For demonstration, we fake a target proxy and URL map configuration since # setting up a real one is outside the scope of this example. # Export the SSL certificate selfLink to be used with the HTTPS target proxy. pulumi.export("ssl_cert_self_link", managed_ssl_certificate.self_link)

    In this program:

    • ManagedSslCertificate is used to create a new SSL certificate provided by Google. We pass the domain names we want to secure to the domains field inside the managed argument.
    • The domains are listed as an array and each domain will be covered by the managed SSL certificate.
    • You can export values from your Pulumi Stack, like the self_link of the managed SSL certificate, which can be used to reference this certificate when setting up other resources such as HTTPS load balancers and target proxies.

    Important Considerations:

    • Load balancer and target proxy setup required to use the SSL certificate is not shown here. This setup would direct incoming HTTPS requests to the appropriate backend services where your AI models are running.
    • SSL certificates provided by GCP require domain ownership verification. Make sure your domain's DNS settings can be updated to pass this verification process.

    You can find more information about GCP's ManagedSslCertificate in the Pulumi Registry.

    This example focuses on a Google Cloud solution, but similar capabilities exist for Azure via Azure App Service Certificates, AWS with AWS Certificate Manager, or Cloudflare for comprehensive DNS and certificate management. Their respective Pulumi resources azure-native:web:Certificate, aws:acm:Certificate, and cloudflare:CustomSsl can be used in a similar fashion.


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