1. Event-Driven AI Orchestrations with AWS MSK


    Event-Driven AI Orchestrations with AWS MSK (Amazon Managed Streaming for Kafka) can be a sophisticated use case, where you use Kafka streams to process events which may then trigger AI/ML (Machine Learning) inference or computations. Pulumi can be very helpful here to set up the foundational infrastructure such as the Kafka clusters, the IAM roles, and possibly integrate with other AWS services needed for the AI workflows, such as AWS Lambda, S3, or SageMaker.

    To implement this orchestration, you'll need several AWS services:

    1. Amazon MSK: Managed Kafka service for streaming data.
    2. AWS IAM: For creating roles and policies that would allow services to interact with each other securely.
    3. Other AWS services: Depending on the exact requirements of your AI orchestration workflow, you might need additional services like AWS Lambda (for running code in response to events), Amazon S3 (for storage), or Amazon SageMaker (for machine learning model training and inference).

    Below is a Pulumi program written in Python that sets up a simple AWS MSK cluster and necessary IAM roles for Event-Driven AI orchestrations. Please note that this is a foundational setup and depending on your specific use case, you may need to configure Kafka topics, hook up AI services, and handle the actual event-driven logic in your application code.

    import pulumi import pulumi_aws as aws # Create an AWS MSK Cluster msk_cluster = aws.msk.Cluster("aiMSKCluster", cluster_name="ai-orchestration-cluster", kafka_version="2.6.1", number_of_broker_nodes=3, broker_node_group_info={ "ebs_volume_size": 100, "instance_type": "kafka.m5.large", "client_subnets": [ # Subnet IDs where you want the broker nodes to be deployed ], "security_groups": [ # Security group IDs ] } ) # Create an IAM Role for MSK msk_role = aws.iam.Role("mskRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "msk.amazonaws.com" } }] }""" ) # Assuming we have an existing SageMaker Model and Endpoint # This is the ARN for the SageMaker endpoint which would be called by a Lambda function sagemaker_endpoint_arn = "arn:aws:sagemaker:us-west-2:123456789012:endpoint/my-endpoint" # Export the Cluster ARN and other important information pulumi.export('MSK Cluster ARN', msk_cluster.arn) pulumi.export('SageMaker Endpoint ARN', sagemaker_endpoint_arn) # More steps would be added to set up event-driven invocation of SageMaker endpoint or Lambda functions # This may include setting up Kafka topics, streams processing logic and more.

    In this code:

    • We create an Amazon MSK cluster with 3 broker nodes.
    • We set up an IAM Role that allows the MSK service to interact with other AWS services as needed.
    • We export the ARN (Amazon Resource Name) of the MSK cluster which uniquely identifies this resource and can be used to set up other configurations.

    Please adjust the subnet_ids and security_groups to match the VPC configuration where you want to deploy the MSK cluster. Also, you would need to configure the Kafka topics and Stream processing logic based on your business logic, which is not covered here. This would require application-level coding rather than infrastructure setup, as Pulumi deals mainly with setting up infrastructure.

    For a more comprehensive setup, you will also want to include error handling, monitoring, logging, and consider best practices around security and governance. Event-driven AI orchestrations can get quite complex, so ensure you plan thoroughly and understand the data flows and interactions between the services.