Orchestrating Machine Learning Workflows with AWS Step Functions

Question

Pulumi · Accepted Answer

Orchestrating machine learning workflows often involves a series of interdependent tasks such as data collection, processing, model training, and evaluation. AWS Step Functions is a serverless orchestration service that makes it easy to sequence AWS services into business-critical applications. By using AWS Step Functions for orchestrating machine learning workflows, you can design and run workflows that stitch together services such as AWS SageMaker (for machine learning tasks) and AWS Lambda (for running your code in response to events) in an AWS environment.

Let's build a Pulumi program in Python that creates an AWS Step Functions state machine. We'll define a simple workflow that involves a placeholder for a machine learning task. The workflow starts an execution of tasks which calls a SageMaker job, but for simplification, we will not include the specific SageMaker job in this example. We will focus on setting up the state machine.

In this Pulumi program, we will:

1. Create an IAM role that AWS Step Functions can assume to execute the tasks provided in the state machine definition.
2. Define the workflow using Amazon States Language in the form of a JSON string.
3. Create the state machine using the workflow definition from step 2 and the IAM role from step 1.

Here's what the Pulumi program looks like:

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

# Step 1: Create an IAM role for AWS Step Functions
sfn_role = aws.iam.Role("sfnRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [
            {
                "Action": "sts:AssumeRole",
                "Effect": "Allow",
                "Principal": {
                    "Service": "states.amazonaws.com"
                }
            }
        ]
    })
)

# Attach the necessary policies to the IAM role
role_policy = aws.iam.RolePolicy("sfnRolePolicy",
    role=sfn_role.id,
    policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "lambda:InvokeFunction",
                    "sagemaker:*"
                ],
                "Resource": "*"
            }
        ]
    })
)

# Step 2: Define the workflow using Amazon States Language
definition = json.dumps({
    "Comment": "A Hello World example of the Amazon States Language using an AWS Lambda function",
    "StartAt": "HelloWorld",
    "States": {
        "HelloWorld": {
            "Type": "Task",
            "Resource": "arn:aws:lambda:REGION:ACCOUNT_ID:function:FUNCTION_NAME",
            "End": True
        }
    }
})

# Step 3: Create a state machine with the IAM role and workflow definition
state_machine = aws.sfn.StateMachine("stateMachine",
    role_arn=sfn_role.arn,
    definition=definition,
)

# Output the ARN of the state machine
pulumi.export('state_machine_arn', state_machine.id)
```

In the code above:

- **IAM Role**: We create an IAM role (`sfnRole`) that AWS Step Functions service can assume. This role has a trust policy for `states.amazonaws.com`, which is the AWS Step Functions service principal.
  
- **Role Policy** (`sfnRolePolicy`): We attach a policy to the IAM role that allows invoking AWS Lambda functions and operating with AWS SageMaker. Replace `"Resource": "*"` with more specific resources to follow the principle of least privilege.

- **Workflow Definition**: We define a simple state machine (`definition`) that has one task state. This uses the Amazon States Language to define the steps of the workflow. The `Resource` field is supposed to contain the ARN of an AWS Lambda function, and this is just a placeholder. You will need to replace `REGION`, `ACCOUNT_ID`, and `FUNCTION_NAME` with the actual values of your AWS Lambda function.

- **State Machine**: We create the state machine (`stateMachine`) with the IAM role and the workflow definition. The state machine will use the IAM role to invoke the tasks defined in the state machine.

The output of this Pulumi program is the ARN of the newly created state machine.

This example does not set up SageMaker tasks or Lambda functions—it simply shows how to set up the skeleton of a Step Functions state machine that can be integrated with other AWS services. In a production scenario, you would also create the actual tasks (e.g., AWS Lambda functions or SageMaker jobs) that the workflow will execute and refer to them in the state machine definition.