1. Scheduling Model Retraining and Deployment Pipelines on AWS MWAA


    To schedule model retraining and deployment pipelines on AWS, we will use the AWS Managed Workflows for Apache Airflow (MWAA) service, which is a managed orchestration service that allows you to execute, monitor, and scale Apache Airflow workflows. By using Apache Airflow's rich set of operators, you can define workflows to retrain machine learning models regularly and deploy them using various AWS services like SageMaker.

    The following program shows how to use Pulumi to provision an AWS MWAA environment, which will be your central hub for orchestrating your model retraining and deployment workflows.

    This Pulumi program does the following:

    1. It sets up an S3 bucket to store the DAGs (Directed Acyclic Graphs) that define your workflows.
    2. It creates an MWAA environment, specifying the S3 bucket created for your DAGs and the execution role that gives your MWAA environment access to AWS resources.
    3. It exports the Airflow URL, which is used to access the Airflow web interface to manage and monitor workflows.

    Here is the Pulumi program to create an AWS MWAA Environment suitable for scheduling model retraining and deployment pipelines:

    import pulumi import pulumi_aws as aws import pulumi_aws_native as aws_native # Create an S3 bucket to store Airflow DAGs dag_bucket = aws.s3.Bucket("airflow-dags") # Define an IAM role for the MWAA environment to interact with AWS services mwaa_execution_role = aws.iam.Role("mwaa-execution-role", assume_role_policy={ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": {"Service": "airflow.amazonaws.com"} }] } ) # Attached policies that give the necessary permissions for the MWAA environment mwaa_policy_attachment = aws.iam.PolicyAttachment("mwaa-policy-attachment", roles=[mwaa_execution_role.name], policy_arn="arn:aws:iam::aws:policy/AmazonMWAAFullAccess" # or a custom policy ARN as needed ) # Create an MWAA environment mwaa_environment = aws_native.mwaa.Environment("model-training-mwaa-environment", name="ModelTrainingEnvironment", dag_s3_path=dag_bucket.id.apply(lambda id: f"dags"), # The path within the S3 bucket where DAGs will be stored # Remaining attributes like execution role ARN, Airflow version, etc. execution_role_arn=mwaa_execution_role.arn, # Add any other MWAA configuration options here ) # Export the Airflow URL pulumi.export("airflow_url", mwaa_environment.webserver_url)

    This program assumes you have the necessary permissions and the AWS CLI is already configured. To run this Pulumi program:

    1. Ensure you have Pulumi and AWS CLI installed and configured on your local machine.
    2. Create a new directory for your project and initialize a new Pulumi program with pulumi new python.
    3. Install the necessary Pulumi AWS SDK using pip install pulumi_aws pulumi_aws_native.
    4. Create a new Python file, copy the code above into it, and save it.
    5. Finally, run pulumi up from the terminal in your project directory to deploy your program.

    Accessing the MWAA web interface:

    • After successfully deploying the MWAA environment with Pulumi, you can navigate to the Airflow URL exported by the program. From there, you can access the Apache Airflow web interface to upload and monitor your DAGs, which define the entire retraining and deployment process.

    Creating and uploading Airflow DAGs:

    • You will need to create DAGs that define your model retraining and deployment steps using Apache Airflow's Python syntax. These DAGs should be uploaded to the S3 bucket provisioned for your workflows.
    • The DAG definition will typically involve using Airflow operators to execute various AWS services like SageMaker for model training and deployment.
    • The DAGs will be automatically detected by the MWAA environment and can be managed through the Airflow web interface provided by AWS.

    Please note that this example primarily sets up the environment and assumes familiarity with Apache Airflow for creating and managing workflows. The actual workflow definition (creating DAGs for model retraining and deployment) should be done within the Airflow platform, following Airflow’s syntax and best practices.