1. Time-based Triggering of Machine Learning Pipelines


    In order to create a time-based triggering of Machine Learning pipelines using Pulumi, we can utilize the respective cloud provider's services to manage machine learning workflows and standard scheduling mechanisms to create triggers based on the time or a particular schedule.

    For AWS, we can utilize aws-native.sagemaker.Pipeline to create and manage SageMaker Model Building Pipelines and aws-native.events.Rule from AWS Events (part of Amazon EventBridge) to trigger these pipelines on a schedule.

    For Azure, similar functionality can be achieved by utilizing azure-native.datafactory.Pipeline to define the data processing workflows and azure-native.datafactory.Trigger to schedule and execute these pipelines.

    For Google Cloud, google-native.datapipelines/v1.Pipeline can be used to define and schedule data pipelines using Google Cloud Data Pipelines.

    These resources enable the orchestration and automation of tasks required for deploying machine learning models and processing large volumes of data. The scheduling capability allows for a pipeline to be executed at specific time intervals, such as daily, weekly, or custom cron schedules, thus facilitating regular updates to machine learning models or processing of new data without manual intervention.

    Let's construct an example using AWS, where we create a SageMaker Pipeline and an EventBridge Rule to trigger it. In this example, assume that the SageMaker Pipeline definition and role are already set up.

    import pulumi import pulumi_aws as aws import pulumi_aws_native as aws_native # Create a SageMaker Pipeline pipeline_name = "my-ml-pipeline" sagemaker_role_arn = "arn:aws:iam::123456789012:role/SageMakerRole" sagemaker_pipeline = aws_native.sagemaker.Pipeline( "sagemakerPipeline", role_arn=sagemaker_role_arn, pipeline_name=pipeline_name, pipeline_definition={ "definitionString": '{"Version": "2020-12-01", "Metadata": {}, "PipelineDescription": "My ML Pipeline", "PipelineName": "my-ml-pipeline", "PipelineDefinition": {}}' } ) # Create an EventBridge Rule to trigger the SageMaker Pipeline on a schedule # For a daily trigger, use a cron expression like 'cron(0 12 * * ? *)' which represents every day at 12:00 PM (UTC) rule = aws.events.Rule( "sagemakerPipelineSchedule", schedule_expression="cron(0 12 * * ? *)", targets=[ aws.events.RuleTargetArgs( arn=sagemaker_pipeline.arn, # Use the Amazon Resource Name (ARN) of the pipeline id="SageMakerPipelineTarget", role_arn=sagemaker_role_arn, # An IAM role ARN with permissions to trigger a SageMaker Pipeline ), ] ) # Output the ARN of the SageMaker Pipeline pulumi.export("pipeline_arn", sagemaker_pipeline.arn) # Output the ARN of the EventBridge Rule pulumi.export("rule_arn", rule.arn)

    In the above program, we created a SageMaker Pipeline for a machine learning workflow using Pulumi's aws_native.sagemaker.Pipeline resource, providing the pipeline definition as a JSON string and specifying a SageMaker role that grants necessary permissions for the ML operations.

    After defining our ML pipeline, we set up an EventBridge Rule that triggers our ML pipeline on a daily schedule at 12:00 PM UTC using the cron syntax. The rule's target is our pipeline, and we use the ARN of our SageMaker Pipeline as the target ARN, which is the identifier EventBridge uses to trigger the pipeline. A role is also provided that has the necessary permissions to invoke actions on other AWS resources.

    The program ends with exporting the ARNs of the created pipeline and the EventBridge Rule. These exports are useful for referencing in AWS and Pulumi, respectively, if additional operations or integrations need to be performed with these resources.

    This is a standard way to automate the execution of ML pipelines on AWS. Similar patterns can be followed for other cloud providers with their respective resources. The key is to define the ML pipeline and a time-based trigger using the cloud provider's native scheduling resources.