AI Model Serving Pipelines with Integrated AWS CodeArtifact

Question

Pulumi · Accepted Answer

To set up AI Model Serving Pipelines with Integrated AWS CodeArtifact, we will follow these steps:

1. **AWS CodeArtifact**: CodeArtifact is a fully managed artifact repository service that makes it easy for organizations of any size to securely store, publish, & share software packages used in their software development process. We'll create a CodeArtifact repository to store and version the model serving packages.

2. **Amazon SageMaker**: SageMaker is a fully managed machine learning service. We will use SageMaker to create and run a model serving pipeline. A Pipeline in SageMaker consists of steps, each running jobs like processing, training, or registering a model.

The following Pulumi Python program creates the necessary infrastructure for this setup:

- **AWS CodeArtifact Domain and Repository**: This is where your machine learning packages will be stored and versioned.
- **SageMaker Pipeline**: This pipeline will define the steps to process data, train the model, and deploy the model for serving.

To run your infrastructure as code using Pulumi, you should first have Pulumi installed and configured with AWS credentials. Then follow the structure below to create the necessary resources in your AWS account:

```python
import pulumi
import pulumi_aws as aws
import json

# Define the AWS CodeArtifact domain.
code_artifact_domain = aws.codeartifact.Domain("my_domain")

# Create a new repository in the AWS CodeArtifact domain.
code_artifact_repository = aws.codeartifact.Repository("my_repository",
    domain=code_artifact_domain.name,
    description="Repository for ML model packages",
)

# Define the SageMaker execution role.
sagemaker_role = aws.iam.Role("sagemaker_execution_role", assume_role_policy=json.dumps({
    "Version": "2012-10-17",
    "Statement": [{
        "Effect": "Allow",
        "Principal": {
            "Service": "sagemaker.amazonaws.com"
        },
        "Action": "sts:AssumeRole",
    }]
}))

# Attach necessary policies to the SageMaker execution role.
sagemaker_role_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker_execution_role_policy_attachment",
    role=sagemaker_role.name,
    policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGEMAKER_FULL_ACCESS
)

# Define an example SageMaker Pipeline.
# Note: The pipeline definition would be normally sourced from an existing definition file.
# We're defining it inline here for simplicity. It should be replaced with your own pipeline definition.
sagemaker_pipeline = aws.sagemaker.Pipeline("my_ml_pipeline",
    role_arn=sagemaker_role.arn,
    pipeline_definition_s3_location={
        "bucket": "my-sagemaker-pipeline-definitions",  # Replace with your S3 bucket name
        "objectKey": "pipeline-definition.json",  # Replace with your pipeline definition object key in S3
    },
    pipeline_description="My machine learning model serving pipeline",
    pipeline_display_name="MyMLModelServingPipeline",
    pipeline_name="MyModelServingPipeline",
    tags={
        "project": "model-serving",
    }
)

# Exporting the CodeArtifact repository endpoint for future use
pulumi.export("code_artifact_repository_endpoint", code_artifact_repository.repository_endpoint)
# Exporting the ARN of the SageMaker pipeline to identify it in the AWS ecosystem
pulumi.export("sagemaker_pipeline_arn", sagemaker_pipeline.arn)
```

In this program:

- We create a **CodeArtifact domain** and **repository** for the ML packages. The repository is where our machine learning models and packages will be stored.
- We define an **IAM Role** for SageMaker execution with the necessary permissions (`AmazonSageMakerFullAccess`) to interact with other AWS services.
- We create a **SageMaker pipeline** that encapsulates the machine learning workflow from data processing to model training and deployment. The pipeline definition is typically a JSON object specifying the workflow steps; for brevity, we've indicated it should be sourced from an S3 bucket, but you'll need to replace `"my-sagemaker-pipeline-definitions"` and `"pipeline-definition.json"` with your actual S3 bucket and object key.

Keep in mind, specific details such as S3 bucket names, object keys, and the pipeline definition will vary based on your environment and should be adjusted accordingly in the program.