1. Ensuring HTTPS for AI Model Serving with Certificate Maps


    To ensure HTTPS for serving an AI model, we will be configuring SSL/TLS certificates to secure the communication between clients and the server where the AI model is hosted. This process involves creating a Certificate Map, which is a collection of SSL/TLS certificates that are used by Google Cloud load balancers and other HTTPS endpoints.

    We will be leveraging Google Cloud Platform (GCP) resources for this task. Specifically, we'll use:

    • CertificateMap: This resource represents a Certificate Map in GCP Certificate Manager that groups together one or more Certificate Map Entries. Each entry can then be associated with one or more managed SSL certificates.
    • CertificateMapEntry: This resource represents an entry within a Certificate Map. It is used to match client requests to specific certificates based on hostname patterns and other criteria.
    • Certificate: Represents an SSL certificate that can be managed by GCP or self-managed by providing the certificate data.
    • DnsAuthorization: If generating a managed certificate, you'll use this to verify domain ownership which is necessary to issue an SSL certificate.

    Here's a program that sets up a Certificate Map for serving an AI model over HTTPS. In this program, we'll assume that we're working with a managed certificate, where Google manages the lifecycle of the certificate for you.

    import pulumi import pulumi_gcp as gcp # Create a DNS authorization to verify the domain ownership. dns_authorization = gcp.certificatemanager.DnsAuthorization("dnsAuthorization", description="DNS authorization for AI model domain", domain="model.example.com", # Replace with your domain ) # Create a Managed Certificate for your domain. managed_certificate = gcp.certificatemanager.Certificate("managedCertificate", type="MANAGED", # Type is managed indicating that GCP will handle the certificate managed=gcp.certificatemanager.CertificateManagedArgs( domains=["model.example.com"], # Replace with your domain dns_authorizations=[dns_authorization.id], ) ) # Create a Certificate Map which groups SSL/TLS certificates. certificate_map = gcp.certificatemanager.CertificateMap("certificateMap", description="A map for SSL certificates for AI Model", ) # Map the Managed Certificate to a specific domain pattern in the Certificate Map Entry. certificate_map_entry = gcp.certificatemanager.CertificateMapEntry("certificateMapEntry", certificate_map=certificate_map.id, hostname="*.example.com", # Replace with your domain pattern certificates=[managed_certificate.id], # Associate the managed certificate ) # Export the certificate map name and entry hostname. These could be used to configure a load balancer or other resources. pulumi.export('certificate_map', certificate_map.name) pulumi.export('certificate_map_entry_hostname', certificate_map_entry.hostname)

    In the provided program:

    1. We create a DNS authorization to prove the ownership of the domain for which we will generate a certificate.
    2. We then create a managed SSL certificate that Google will automatically renew. The certificate is for the domain "model.example.com" (you should replace this with your actual domain).
    3. We create a Certificate Map, which is a resource that allows us to group certificates. This would be useful when working with multiple certificates and domains.
    4. We create a Certificate Map Entry to associate the newly created managed certificate with the domain pattern (in this case, any subdomain of "example.com").
    5. Finally, we export the certificate map and entry hostname for use with other resources, like a load balancer or a gateway to the AI model service.

    To use the code snippet, you'll need a Google Cloud Platform account and project. Also, ensure you've set up Pulumi with GCP configuration and have permissions to create these resources.

    Remember that this is a foundational setup for ensuring HTTPS for an AI model serving endpoint. Typically, you would also need a load balancer and other networking configurations to route traffic securely to your AI model's server. Ensure that your AI model's serving service is configured to use the SSL certificates from the Certificate Map.