1. Automated ML Model Rollouts with Predictive Scaling


    Automated Machine Learning (ML) Model Rollouts with Predictive Scaling is a sophisticated scenario wherein machine learning models are deployed into production and scaled automatically based on anticipated load. Predictive scaling uses machine learning to analyze historical load patterns and predict future demand, thereby ensuring that sufficient resources are available to handle incoming traffic, while minimizing over-provisioning and cost.

    In cloud environments like AWS, this can be achieved by using services such as AWS Auto Scaling with predictive scaling policies. Using Pulumi, we can automate the setup of such an infrastructure.

    Below is a Pulumi program in Python that sets up an AWS Auto Scaling Group with a Predictive Scaling Configuration. Here's what it does:

    1. AWS Auto Scaling Group: This is a collection of EC2 instances managed by AWS to scale in or out automatically. Here, the application running the ML model will be deployed.

    2. Predictive Scaling: This feature anticipates the right number of EC2 instances required in the Auto Scaling Group by using machine learning models to predict the future load based on historical data.

    3. Scaling Plan: A scaling plan tells AWS Auto Scaling how to manage resource scaling for the multiple resources. The plan uses predictive scaling as well as dynamic scaling (which responds to changing load).

    Let's build the program:

    import pulumi import pulumi_aws as aws # Assuming the application source is provided and versioned through AWS CodeDeploy. application_source = aws.codedeploy.Application("applicationSource") # The predictive scaling configuration is achieved through the Auto Scaling Plans. # A scaling plan is created to manage scaling for the Auto Scaling Group. scaling_plan = aws.autoscalingplans.ScalingPlan( "scalingPlan", application_source={ "tagFilters": [{ "key": "AppName", "values": [application_source.name], # Tag filters can be used to include only specific resources }], }, scaling_instructions=[aws.autoscalingplans.ScalingPlanScalingInstructionArgs( max_capacity=10, # Maximum number of instances min_capacity=1, # Minimum number of instances resource_id=f"autoScalingGroup:{application_source.name}", # Resource to apply scaling to; assumed as an ASG scalable_dimension="autoscaling:autoScalingGroup:DesiredCapacity", service_namespace="autoscaling", # The namespace of the AWS service (Auto Scaling Group) target_tracking_configurations=[aws.autoscalingplans.ScalingPlanTargetTrackingConfigurationArgs( target_value=70.0, # Target value for the metric (CPU utilization) predefined_scaling_metric_specification=aws.autoscalingplans.ScalingPlanPredefinedScalingMetricSpecificationArgs( predefined_scaling_metric_type="ASGAverageCPUUtilization" ), disable_scale_in=False, # Indicates whether scale in by the target tracking policy is disabled scale_out_cooldown=300, # Cooldown period before another scale out event can occur scale_in_cooldown=300, # Cooldown period before another scale in event can occur )] )]) # Output the scaling plan's ID which we can use to monitor and manage the scaling of our ML model deployment. pulumi.export("scaling_plan_id", scaling_plan.id)

    To explain the setup:

    1. We define an aws.codedeploy.Application which represents the source of our application. This could be your machine learning model wrapped in a deployable web application, for instance. The tagFilters are used to select the resources that the scaling plan should apply to.

    2. The aws.autoscalingplans.ScalingPlan is where we set up the predictive scaling. We provide a minimum and maximum capacity to define the range of scaling we expect for our application.

    3. The resource_id is a link to the actual Auto Scaling Group that will manage our EC2 instances running the ML model. (Note that for this program to run, you would already have an ASG set up with the specified name, which is obtained from the application_source in this case).

    4. We use the target_tracking_configurations to provide a list of criteria that AWS should use to scale resources. Here, we're using CPU utilization as our metric, aiming to maintain an average CPU utilization of 70% across our instances.

    5. The predefined_scaling_metric_specification defines the actual metric we are targeting, in our case, average CPU Utilization.

    6. The disable_scale_in and scale_*_cooldown settings fine-tune the responsiveness of the scaling: when it scales out (adds instances), and when it scales back in (removes instances).

    Note: This code assumes that you have an existing AWS CodeDeploy application and Auto Scaling Group with matching tags. You'll need to replace certain placeholders like application_source.name with your actual resource names and identifiers. If you're starting from scratch, you would need to define those resources in your Pulumi program first.

    Once deployed, AWS Auto Scaling will take care of scaling your resources up or down based on the load predictions, ensuring your ML application remains responsive and cost-effective.