1. Automated ML Pipeline Artifact Promotion via S3 ObjectCopy


    To achieve an automated ML (Machine Learning) pipeline artifact promotion using AWS S3 via object copy, you would typically have an initial S3 bucket where your ML artifacts are stored after training, and a subsequent S3 bucket where promoted artifacts are copied to for production use.

    Promotion in this sense refers to the process of moving or copying a validated or approved artifact from one stage of the pipeline (e.g., development or staging) to another stage (e.g., production) in a controlled manner. In the context of AWS S3, this could mean copying an object from one S3 bucket to another, possibly while applying additional S3 features such as object tagging and access control during the copy operation.

    In Pulumi, this could be orchestrated by defining resources that represent the two S3 buckets and configuring an ObjectCopy operation to copy artifacts from the source to the destination bucket. The ObjectCopy operation may be triggered by some condition or integration with another service like AWS CodePipeline or AWS Lambda.

    Let's write a Pulumi program in Python that defines the S3 buckets and the object copy operation. The program will consist of:

    • Two S3 buckets: One for the initial storage of ML artifacts and another one for promoted artifacts.
    • An ObjectCopy resource that copies files from the source bucket to the destination bucket.

    Note: The actual triggering of object copy will likely involve other AWS services, such as invoking Lambda functions or through a CI/CD pipeline and is beyond the scope of the code below.

    Here's the program:

    import pulumi import pulumi_aws as aws # Define the source bucket where ML artifacts are initially stored source_bucket = aws.s3.Bucket("source-bucket") # Define the destination bucket to which ML artifacts will be promoted destination_bucket = aws.s3.Bucket("destination-bucket") # Copy an object from the source bucket to the destination bucket # For the purpose of this example, we assume a specific object key 'ml-artifact' # In a real-world scenario, you would likely parameterize this or use dynamic references artifact_copy = aws.s3.ObjectCopy("artifact-copy", bucket=destination_bucket.id, # The bucket to copy to key="ml-artifact", # The key/object name in the destination bucket source=pulumi.Output.concat(source_bucket.arn, "/ml-artifact"), # Reference to the source bucket object ARN acl="private", # Access control list (ACL) for the copied object tags={"Environment": "Production"} # Optional tags for tracking ) # Output the URLs of the source and destination artifacts for verification pulumi.export("source_artifact_url", pulumi.Output.concat("s3://", source_bucket.id, "/ml-artifact")) pulumi.export("destination_artifact_url", pulumi.Output.concat("s3://", destination_bucket.id, "/ml-artifact"))


    • We begin by importing the required Pulumi AWS SDK modules.
    • source_bucket and destination_bucket are S3 bucket resources. These buckets will respectively act as the storage for the initial and promoted ML artifacts.
    • The artifact_copy resource is of type ObjectCopy (aws.s3.ObjectCopy) from the AWS package. It represents the operation to copy an artifact from the source bucket to the destination bucket.
      • We specify the bucket property to indicate the destination bucket's ID.
      • The key property defines the object name in the destination bucket.
      • The source property is the full source object identifier, which combines the source bucket's ARN (Amazon Resource Name) with the object key. We use Pulumi's Output.concat to dynamically construct this identifier, which is dependent on the source_bucket creation.
      • The acl is set to private, which makes the copied object accessible only by the owner by default. You can alter this depending on the accessibility requirements.
      • tags can be used to set metadata on the copied object, helpful for auditing or categorization of artifacts in the destination bucket.
    • Finally, we use pulumi.export to output the URLs for the artifacts in both the source and destination buckets, which can be used for verification or further processing.

    Keep in mind that this code sets up the infrastructure for copying bucket objects, but the actual logic for triggering the copy, possibly in response to events (like a successful ML model training job), would involve additional AWS services and is not covered here. The ObjectCopy action here assumes a manual trigger for simplicity. In a full-fledged ML pipeline, you would integrate more sophisticated triggers and logic for artifact promotion.