Automated SSL Certificate Management for AI Model Serving Endpoints

Question

Pulumi · Accepted Answer

Automated SSL Certificate Management is essential for ensuring secure communication between clients and AI model serving endpoints. SSL certificates validate the identity of the server and encrypt the data transmitted between the server and clients. Managing SSL certificates can be complex and error-prone if done manually, but cloud providers offer services that can automate the process.

For serving AI models, you might want to consider a combination of load balancers, compute instances to serve the models, and managed SSL certificates to automate SSL/TLS for your endpoints. Since you haven't specified a cloud provider, I will provide an example using Google Cloud Platform as it offers a managed service for SSL certificates.

Here's a detailed breakdown of what the code will do:

Create a managed SSL certificate, which Google Cloud will automatically provision and renew, securing your AI Model Serving endpoint.
Create a target proxy to use the SSL certificate, which will determine the type of client connection the load balancer will accept and use.
Create a regional backend service where you’ll define the AI model serving backend, for example, a group of compute instances with a configured instance group or network endpoint group.
Set up a URL map to route incoming requests to your backend service.
Establish a forwarding rule that will use the managed SSL certificate and ensure that the AI Model Serving endpoint receives traffic over HTTPS.

Here is the Python program that sets up this infrastructure using Pulumi:

import pulumi
import pulumi_gcp as gcp

# Create a managed SSL certificate which automatically provisions and renews.
managed_ssl_certificate = gcp.compute.ManagedSslCertificate(
    "managed-ssl-cert",
    name="managed-ssl-cert",
    managed={
        "domains": ["your-model-serving-domain.com"]  # Replace with your domain
    }
)

# Create a target proxy that will make use of the SSL certificate.
target_ssl_proxy = gcp.compute.TargetSslProxy(
    "target-ssl-proxy",
    name="target-ssl-proxy",
    ssl_certificates=[managed_ssl_certificate.id],
    service=gcp.compute.BackendService.get("backend-service").self_link  # Define your backend service reference
)

# Define a backend service.
# This service should point to your AI Model Serving backend, such as a group of compute instances.
# Assuming you've already created an instance group or network endpoint group for your servers, reference it here.
backend_service = gcp.compute.BackendService(
    "backend-service",
    name="backend-service",
    backends=[{
        "group": "<Instance Group or NEG self_link>",  # Replace with the self_link of your instance group or NEG
    }],
    health_checks=[<Your_Health_Check_Name>]  # Replace with your health check name
)

# Create a URL map to direct traffic to the backend service based on the incoming request path.
url_map = gcp.compute.URLMap(
    "url-map",
    name="url-map",
    default_service=backend_service.self_link
)

# Create a forwarding rule that utilizes the SSL Proxy and Managed SSL Certificate to accept HTTPS traffic.
forwarding_rule = gcp.compute.GlobalForwardingRule(
    "https-forwarding-rule",
    name="https-forwarding-rule",
    target=target_ssl_proxy.self_link,
    port_range="443",
    ip_protocol="TCP",
    ip_address="<Your Global IP Address>"  # Replace with your reserved global IP
)

# Export the managed SSL Certificate and Forwarding Rule IP to be accessible later.
pulumi.export("ssl_certificate", managed_ssl_certificate.self_link)
pulumi.export("forwarding_rule_ip", forwarding_rule.ip_address)

This Pulumi program sets up an HTTPS load balancer with automated SSL certificate management for your AI model serving endpoints. You will need to replace values like your-model-serving-domain.com, <Instance Group or NEG self_link>, <Your_Health_Check_Name>, and <Your Global IP Address> with your specific configuration. It assumes you have set up required resources like instance groups and health checks already. If not, you need to define those resources in your program as well.

Remember to execute this Pulumi program within a Google Cloud project and authenticate with appropriate credentials that have permission to create these resources. After deploying this program with Pulumi, the managed SSL certificate will be automatically provisioned and renewed by Google Cloud, securing your AI model serving endpoints with SSL/TLS encryption without manual intervention.