Automated ML Model Retraining Workflows

Question

Pulumi · Accepted Answer

To create an automated machine learning model retraining workflow, you can use cloud services like AWS SageMaker or Azure Machine Learning.

The goal of an automated ML model retraining workflow is to periodically train machine learning models with new data. This ensures that models are up-to-date and performant. The process typically includes steps like data preprocessing, model training, evaluation, and deployment.

Using Pulumi, you can define the infrastructure that orchestrates these steps. Below is an example that demonstrates how to use AWS SageMaker to create a machine learning pipeline for model retraining.

The key components of the AWS solution include:
- **SageMaker Pipeline**: Defines the steps of the machine learning workflow.
- **SageMaker Model**: Represents the trained ML model that is being retrained.
- **SageMaker Training Job**: Specifies the training algorithm and input configuration.
- **Lambda Function**: (Optional) Could trigger the pipeline based on certain events.

Let's look at a Pulumi program that sets up a basic AWS SageMaker pipeline for retraining a machine learning model:

```python
import pulumi
import pulumi_aws as aws
import pulumi_aws_native as aws_native

# Role for SageMaker to access AWS resources
sagemaker_role = aws.iam.Role("sagemaker-role",
    assume_role_policy="""{
      "Version": "2012-10-17",
      "Statement": [{
        "Effect": "Allow",
        "Principal": {"Service": "sagemaker.amazonaws.com"},
        "Action": "sts:AssumeRole"
      }]
    }"""
)

# Policy attachment for the role defining permissions
sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemaker-policy-attach",
    role=sagemaker_role.name,
    policy_arn=aws.iam.ManagedPolicy.AMAZON_SAGE_MAKER_FULL_ACCESS
)

# SageMaker pipeline definition
# Replace the `pipeline_definition` with your model training steps
# This should be defined using the SageMaker Pipeline DSL or JSON definition
pipeline_definition = """{
    "Version": "2020-12-01",
    "Metadata": {},
    "Parameters": [],
    "PipelineDescription": "My SageMaker pipeline for model retraining",
    "Stages": [
        {
            "Name": "Training",
            "Type": "Training"
            # More configuration goes here...
        }
        # Add other stages like Model Evaluation, Conditional Steps, etc.
    ]
}"""

# SageMaker pipeline resource
sagemaker_pipeline = aws_native.sagemaker.Pipeline("sagemaker-pipeline",
    pipeline_name="my-sagemaker-pipeline",
    role_arn=sagemaker_role.arn,
    pipeline_definition=pipeline_definition,
    tags={
        "Environment": "development"
    }
)

# Output the ARN of the SageMaker Pipeline
pulumi.export("pipeline_arn", sagemaker_pipeline.arn)
```

In the above program:

1. We created a SageMaker role granting necessary permissions for SageMaker services to interact with other AWS resources.
2. We attached the SageMaker full access policy to this role.
3. We defined a SageMaker Pipeline with a simplification for demonstration, where you would replace the `pipeline_definition` with steps specific to your model retraining process, written in the SageMaker Pipeline DSL or JSON format.
4. Finally, we export the ARN of the SageMaker Pipeline which can be used to identify the pipeline later on AWS.

In a real-world scenario, you would define steps for data extraction, model training (specifying algorithm and data source), evaluation (comparing model metrics), and conditionally deploying the model if it meets your criteria within the `pipeline_definition`.

For triggering the pipeline, you can set up a scheduled event (e.g., with AWS EventBridge), or an AWS Lambda function listening to data change events in your data storage, which can then start the pipeline execution.

Remember to replace the simplified `pipeline_definition` in the code with the actual definition that describes your ML pipeline steps. You can define these steps using AWS SageMaker's Python SDK to create a domain-specific language (DSL) or in JSON format.

For more in-depth information on AWS SageMaker pipelines and how to define a machine learning workflow, you can refer to the [AWS SageMaker Pipeline documentation](https://www.pulumi.com/registry/packages/aws-native/api-docs/sagemaker/pipeline/). For Azure solutions, similar concepts apply, but you would use resources from the Azure Machine Learning Service.