1. Fixed-IP Inference Endpoints for AI Applications


    To create fixed IP inference endpoints for AI applications, you need to deploy a service that receives data, runs inference using a pre-trained machine learning model, and sends back predictions. The fixed IP is necessary for cases where clients expect the endpoints to have consistent addresses for whitelisting or network configuration purposes.

    The specific details can depend greatly on the cloud provider. However, we will go through creating an AI inference endpoint using Google Cloud Platform's Vertex AI service as an example. Vertex AI allows you to deploy and serve machine learning models with Google Cloud resources, such as Compute Engine instances or custom machine types.

    In this example, we will create an AI Endpoint using Pulumi's GCP provider, which can serve predictions from an ML model deployed in the cloud. The pulumi_gcp.vertex.AiEndpoint resource represents a Vertex AI endpoint that can be attached with a deployed model to serve online predictions. We won't cover the model training and deployment in this example, but rather focus on the infrastructure setup for serving the model.

    Here's how to create a fixed IP inference endpoint with Pulumi:

    import pulumi import pulumi_gcp as gcp # Create a VPC network to host the resources. network = gcp.compute.Network("network") # Provisions a subnet where the resources will live. subnet = gcp.compute.Subnetwork("subnet", network=network.id, ip_cidr_range="") # Reserve a static external IP address for your inference endpoint. static_ip = gcp.compute.GlobalAddress("static-ip") # Create a Vertex AI Endpoint. # The endpoint is where the online prediction requests are sent. ai_endpoint = gcp.vertex.AiEndpoint("ai-endpoint", project=gcp.config.project, location="us-central1", # Specify the region you prefer display_name="fixed-ip-inference-endpoint") # Output the fixed IP address. pulumi.export("fixed_ip_address", static_ip.address) # Output the endpoint URL for sending prediction requests. pulumi.export("endpoint_url", ai_endpoint.network)

    In this code:

    • We create a VPC network and a subnet. These are foundational networking resources that would contain the AI inference service within a controlled network space in GCP.
    • We reserve a static global IP address. The static IP is crucial for creating an endpoint with a fixed IP.
    • We then set up a Vertex AI Endpoint, which acts as the gateway for serving the online prediction requests. Note that this assumes the model is already deployed and configured. We specify the project and location according to the preferences.
    • Lastly, we export the static IP address and the URL of the endpoint as stack outputs. These can be used to access the endpoint and could be shared with the clients that will be consuming the AI service.

    Keep in mind that this example sets up the networking infrastructure for hosting an AI endpoint. You would need to deploy a machine learning model to this endpoint and potentially configure additional settings, such as traffic splitting, authentication, and monitoring. The Google Cloud SDK and the Vertex AI API can be used to manage the configuration of the model deployment to the endpoint you created with Pulumi.