1. ALB Routing for A/B Testing of Machine Learning Models


    Routing traffic for A/B testing using an Application Load Balancer (ALB) requires deploying two versions of a machine learning model. The ALB will then distribute incoming traffic between these two variants. The approach includes creating a target group for each version of the model, configuring listener rules to split traffic based on certain criteria (e.g., weighted randomness), and monitoring the results to determine the performance of each version.

    Below is a Pulumi program written in Python that sets up an AWS ALB for A/B testing of machine learning models. Assume that we've already trained two models, and we have two different HTTP endpoints ready to serve these models, which we'll route traffic to with the ALB. This program creates an ALB, two target groups, and listener rules to distribute the incoming requests between two target groups.

    In this example, we'll use the awsx package because it provides high-level components that make working with AWS resources like an ALB and target groups easier.

    import pulumi import pulumi_awsx as awsx # Create an Application Load Balancer for routing traffic. alb = awsx.lb.ApplicationLoadBalancer("abTestAlb") # Define target group A for Model Version A. target_group_a = awsx.lb.ApplicationTargetGroup( "abTestTargetGroupA", port=80, protocol="HTTP", vpc=alb.vpc_id, # other properties might be needed, such as health check configuration and target type (instance, ip, or lambda) ) # Define target group B for Model Version B. target_group_b = awsx.lb.ApplicationTargetGroup( "abTestTargetGroupB", port=80, protocol="HTTP", vpc=alb.vpc_id, # other properties might be needed, such as health check configuration and target type (instance, ip, or lambda) ) # Create a listener for incoming traffic on the ALB. listener = alb.create_listener("abTestListener", port=80, default_action=awsx.lb.ListenerDefaultAction.forward([target_group_a])) # Attach listener rules to route traffic to different target groups based on weight. # 50% to target group A, 50% to target group B for simplicity. listener.add_rule("abTestRoutingRule", actions=[ awsx.lb.ListenerRuleAction.forward( target_group_tuples=[ awsx.lb.ListenerRuleForwardTargetGroupWeightTuple(target_group=target_group_a, weight=1), # weight can be adjusted awsx.lb.ListenerRuleForwardTargetGroupWeightTuple(target_group=target_group_b, weight=1), # weight can be adjusted ]), ], conditions=[], # Define conditions here if needed (e.g., path-based, host-based, headers, etc.) ) # Export the ALB endpoint URL so it can be accessed. pulumi.export("albEndpoint", alb.endpoint)

    In the code:

    • First, we import the required pulumi and pulumi_awsx modules.
    • We create an ApplicationLoadBalancer that automatically sets up the network to receive HTTP traffic.
    • We then create two ApplicationTargetGroup instances for the two model versions, target_group_a for Model Version A and target_group_b for Model Version B. Each group will get traffic from the ALB; you need to connect the actual HTTP endpoints serving your models to these target groups.
    • After that, we create an ALB listener on port=80 to inspect incoming traffic, with a default rule to forward it to target_group_a.
    • Next, we define a listener rule with add_rule, specifying that the listener should distribute incoming traffic across both target groups based on the specified weights. With weights both set to 1, the distribution is equal at 50% for each target group, but you can adjust these weights based on your A/B testing plan.
    • Finally, we export the endpoint URL of the ALB, which you can give to users to access the A/B testing setup.

    This is just a starting point. In actual implementation, you'll need to adjust details like the health checks of the target groups, the precise distribution of traffic, and possibly complex routing conditions based on certain HTTP headers or URL paths.

    Additionally, remember to replace the placeholders for the actual connections to your HTTP endpoints serving the models to fully integrate with your machine learning model serving infrastructure.