1. Traffic Routing for A/B Testing ML Models


    To implement traffic routing for A/B testing machine learning models, we would ideally put our model deployments behind a load balancer that can direct traffic to different model versions based on predefined rules or weights. This allows us to experiment with different models and compare their performance in a production-like environment.

    For the purpose of this explanation, I'll demonstrate setting up traffic routing with AWS services, because AWS provides an extensive set of tools that can facilitate such a setup. We'll be using AWS Elastic Load Balancing (ELB) to distribute incoming application traffic across multiple deployments (e.g., EC2 instances or ECS tasks), where different ML model versions are hosted. Additionally, AWS Route 53 can be used to manage DNS settings to further control and distribute traffic for A/B testing purposes.

    The following Pulumi program creates an Application Load Balancer (ALB), two target groups for different versions of an ML model, and sets up listener rules for routing traffic between these target groups based on specified conditions.

    Here is how you set up traffic routing for A/B testing with Pulumi in Python:

    1. Create two AWS EC2 instances where each instance runs a different version of the ML model.
    2. Set up two Target Groups: one for each EC2 instance running the different model versions.
    3. Create an Application Load Balancer (ALB) which listens for incoming traffic on a particular port.
    4. Define Listener Rules for the ALB to route the traffic to different Target Groups based on certain conditions (e.g., URL path, query parameters, HTTP headers).

    Let's go through the code for setting this up:

    import pulumi import pulumi_aws as aws # Note: The following Pulumi code assumes that you have already set up the necessary VPC and subnets. # You would also need to configure the AWS provider with the correct region and credentials. # Define two different target groups for A/B testing model_a_target_group = aws.lb.TargetGroup("model-a-target-group", # other target group configuration like health checks, vpc_id, etc. ) model_b_target_group = aws.lb.TargetGroup("model-b-target-group", # other target group configuration like health checks, vpc_id, etc. ) # Create an Application Load Balancer app_load_balancer = aws.lb.LoadBalancer("app-load-balancer", # Load balancer configuration like subnets, security groups, etc. ) # Create a listener for the application load balancer listener = aws.lb.Listener("listener", load_balancer_arn=app_load_balancer.arn, # Listener configuration like default actions, port, etc. ) # Listener rule to direct traffic to Model A's target group based on a condition (e.g., header or path pattern) model_a_rule = aws.lb.ListenerRule("model-a-rule", actions=[{ "type": "forward", "target_group_arn": model_a_target_group.arn, }], conditions=[{ # Define the condition here, such as a path pattern or header value }], listener_arn=listener.arn, priority=10, # Lower number takes precedence ) # Listener rule to direct traffic to Model B's target group model_b_rule = aws.lb.ListenerRule("model-b-rule", actions=[{ "type": "forward", "target_group_arn": model_b_target_group.arn, }], conditions=[{ # Define the condition here, such as a path pattern or header value different from model_a_rule }], listener_arn=listener.arn, priority=20, # Higher number than model_a_rule ) # Export the DNS name of the ALB for access pulumi.export("alb_dns_name", app_load_balancer.dns_name)

    In the above program:

    • We set up two target groups for A/B testing, one for each model version.
    • We create an Application Load Balancer (ALB) to listen for incoming traffic.
    • We define two listener rules to route traffic to the target groups based on specified conditions such as URL path or HTTP headers.

    With this setup, you can start directing a portion of your traffic to a new ML model and compare its performance against the current model. This helps make informed decisions backed with real traffic data, improving the reliability of model deployments.

    Do ensure that you have the necessary infrastructure in place, like EC2 instances running the ML models, VPC, and subnets before deploying this Pulumi program.