1. Smart Routing for Improved AI Model Accessibility with NS1

    Python

    To create a smart routing setup for AI model accessibility, you would typically need to use a DNS or global traffic management service that can route traffic based on various rules and conditions such as latency, geography, the health of your endpoints, and more. NS1 is a data-driven DNS and traffic management service that provides such functionalities. However, I'll show you how to achieve a similar goal using AWS's Global Accelerator service as there isn't a direct integration with NS1 in Pulumi just yet.

    AWS Global Accelerator is a networking service that improves the availability and performance of the applications that you offer to your global users. It works by directing traffic to optimal endpoints over the AWS global network. When the internet is congested, Global Accelerator's automatic routing optimizations will help keep packet loss, jitter, and latency consistently low.

    The following Pulumi Python program sets up a Global Accelerator that redirects traffic to two different AWS regions based on performance. While this doesn't include AI model serving endpoints specifically, it sets the stage for any application endpoint, including one that would serve AI models:

    1. AWS Global Accelerator: This is the main component that routes traffic smartly to the best endpoints.
    2. Endpoint Groups: These are sets of endpoints, such as Elastic IP addresses, that Global Accelerator can route traffic to. I've set up two groups, each corresponding to a different AWS region. Similarly, you could configure them to point to the infrastructure serving your AI model.
    3. Listeners: These define how the Global Accelerator receives and routes traffic to the optimal endpoint groups based on the client's location.

    Let's write the code:

    import pulumi import pulumi_aws as aws # Set up an AWS Global Accelerator. accelerator = aws.globalaccelerator.Accelerator("aiModelAccelerator", enabled=True, ip_address_type="IPV4") # To demonstrate smart routing, we set up two endpoint groups # in different AWS regions. In a real scenario, these could be # EC2 instances or ECS tasks that serve your AI model. # First endpoint group in the US-West-1 region. endpoint_group_us_west = aws.globalaccelerator.EndpointGroup("aiModelEndpointGroupUSWest", accelerator_arn=accelerator.arn, endpoint_group_region="us-west-1", endpoint_configurations=[{ "endpointId": "eip-12345678", # Example Elastic IP address "weight": 128, # Traffic weight assigned to this endpoint }]) # Second endpoint group in the US-East-1 region. endpoint_group_us_east = aws.globalaccelerator.EndpointGroup("aiModelEndpointGroupUSEast", accelerator_arn=accelerator.arn, endpoint_group_region="us-east-1", endpoint_configurations=[{ "endpointId": "eip-87654321", # Another example Elastic IP address "weight": 128, # Traffic weight assigned to this endpoint }]) # Listener that redirects traffic to the endpoint groups. listener = aws.globalaccelerator.Listener("aiModelListener", accelerator_arn=accelerator.arn, client_affinity="NONE", # No client affinity in this example. protocol="TCP", # Protocol used for routing (TCP/UDP). port_ranges=[{ # Port range to listen on for traffic. "fromPort": 80, "toPort": 80, }]) # The full URL to access the accelerator and by extension your AI model endpoint will be # dependent on your application's setup (for example, an Application Load Balancer or directly an EC2 instance). # You would typically use the DNS name provided by the Global Accelerator along with any path specific to your application. # Export the Global Accelerator's DNS name. pulumi.export('accelerator_dns_name', accelerator.dns_name)

    This program sets up a basic AWS Global Accelerator configuration that forms the infrastructure for smart routing to your application endpoints, which can be configured to serve an AI model. The accelerator_dns_name export will give you the DNS name you'd use to access your applications.

    For more information about the resources we've used:

    Remember, this setup will work uniformly whether your users are accessing a web application or an endpoint serving AI predictions; it's about directing them to the best-performing and most available endpoint. You can refine traffic routing based on further AWS Global Accelerator settings, accommodating specific requirements like traffic dialing, health checks, and client affinity.


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