📌 Part 2: Creating AWS Lambda Function and Role for SageMaker Endpoint Invocation (with Screenshot Guides)

1. Create IAM Role for Lambda

Purpose: This role allows the Lambda function to invoke the SageMaker endpoint.

Steps:

• Go to the AWS Console → IAM → Roles → Create Role.
• Trusted Entity Type: Choose AWS Service.
• Use Case: Select Lambda.
• Click Next to Permissions step.

✅ Screenshot Tip:\

✅ Screenshot the Create role page → Select Lambda as trusted entity

2. Attach SageMaker Invoke Permission Policy

Purpose: Grant the Lambda function permission to invoke the SageMaker endpoint.

Steps:

• In the same role creation process, click Create policy to open a new tab.
• Switch to the JSON tab and paste the following policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:InvokeEndpoint"
            ],
            "Resource": [
                "arn:aws:sagemaker:us-east-2:YOUR_ACCOUNT_ID:endpoint/lamini-t5-gpu-endpoint"
            ]
        }
    ]
}

✅ Note: Replace the Resource value with the endpoint ARN you obtained in Part 1.

• Click Next, give it a name like SageMakerInvokePolicy, and click Create Policy.
• Go back to the role creation tab, refresh and attach the newly created policy.

✅ Screenshot Tip:\

✅ Screenshot the JSON policy creation step showing the custom policy\

✅ Screenshot the attached policies before creating the role

• Name the role generate-text-lamini-role and create it.
• After creation, click on the role and copy the Role ARN — you will need this when setting up Lambda.

✅ Screenshot Tip:\

✅ Screenshot the Role ARN after creation

3. Create AWS Lambda Function

Purpose: Lambda serves as the backend to accept requests and forward them to the SageMaker endpoint.

Steps:

• Go to AWS Console → Lambda → Create function.
• Author from scratch:
  • Function name: generate-text-lamini
  • Runtime: Python 3.10
  • Execution Role: Choose Use an existing role → Select generate-text-lamini-role.
• Click Create Function.

✅ Screenshot Tip:\

✅ Screenshot the Create Lambda Function screen showing name, runtime, and role

4. Lambda Function Code

In the Function code section of your Lambda function, replace the default code with the following:

import boto3
import json

client = boto3.client('sagemaker-runtime')

ENDPOINT_NAME = 'lamini-t5-gpu-endpoint'  # Name of your SageMaker endpoint


def lambda_handler(event, context):
    try:
        prompt = event.get('prompt', '')

        if not prompt:
            return {
                'statusCode': 400,
                'body': json.dumps({'error': 'Prompt not provided'})
            }

        response = client.invoke_endpoint(
            EndpointName=ENDPOINT_NAME,
            ContentType='application/json',
            Body=json.dumps({"inputs": prompt})
        )

        result = json.loads(response['Body'].read().decode())

        return {
            'statusCode': 200,
            'body': json.dumps(result)
        }
    except Exception as e:
        return {
            'statusCode': 500,
            'body': json.dumps({'error': str(e)})
        }

✅ Screenshot Tip:\

✅ Screenshot the Function code editor with the pasted code

• Click Deploy to save your Lambda function.

✅ Screenshot Tip:\

✅ Screenshot after clicking Deploy

✅ Summary Checklist:

• ✅ Created IAM role for Lambda with SageMaker invoke permissions.
• ✅ Created Lambda function using the correct IAM role.
• ✅ Configured Lambda function code to forward prompts to the SageMaker endpoint.
• ✅ Deployed Lambda function and saved Lambda ARN for API Gateway setup (Part 3).

✅ Next Step: Proceed to Part 3 to configure API Gateway and expose the Lambda function via a public HTTP endpoint.