Automated ML Model Retraining with S3 Events and Lambda

Question

Pulumi · Accepted Answer

Automating machine learning model retraining can be an essential part of maintaining a high-performing predictive system. When you have new data arriving into an AWS S3 bucket, an AWS Lambda function can be triggered to start the retraining process of the machine learning model.

Here's how you can set up such a pipeline using Pulumi:

1. **AWS S3 Bucket**: This is where your new training data will be uploaded. An event notification can be set on the S3 bucket to trigger a Lambda function when new data is added. 
   
2. **AWS Lambda**: This function is the core of the automation. It will be executed in response to new data being added to the S3 bucket. The function can perform any number of tasks including pre-processing data, starting the model training job, and updating the model once training is complete.
   
3. **IAM Role and Policy**: The Lambda function will need permission to access S3 and perform operations such as starting training jobs. An IAM Role with the necessary policies will be created and attached to the Lambda function.

This program will demonstrate setting up an S3 bucket with event notifications to trigger a Lambda function and the associated IAM Role and permissions for that Lambda function. The actual model training code within the Lambda function is something you would need to provide based on your specific needs and machine learning framework.

Let's create the Pulumi program in Python:

```python
import pulumi
import pulumi_aws as aws

# Create an AWS S3 bucket for storing training data
training_data_bucket = aws.s3.Bucket("trainingDataBucket")

# IAM Role for Lambda Function
lambda_execution_role = aws.iam.Role("lambdaExecutionRole", assume_role_policy=json.dumps({
    "Version": "2012-10-17",
    "Statement": [{
        "Action": "sts:AssumeRole",
        "Principal": {
            "Service": "lambda.amazonaws.com"
        },
        "Effect": "Allow",
        "Sid": ""
    }]
}))

# Attach policies to the role to allow access to S3 and CloudWatch Logs for the lambda
aws.iam.RolePolicyAttachment("lambdaS3Access",
    role=lambda_execution_role.name,
    policy_arn=aws.iam.ManagedPolicy.AMAZON_S3_FULL_ACCESS.value)

aws.iam.RolePolicyAttachment("lambdaLogging",
    role=lambda_execution_role.name,
    policy_arn=aws.iam.ManagedPolicy.SERVICE_ROLE_FOR_LAMBDA_BASIC_EXECUTION_ROLE.value)

# Create the Lambda function
ml_model_retraining_lambda = aws.lambda_.Function("mlModelRetrainingLambda",
    runtime="python3.8",
    code=pulumi.FileArchive("./lambda"), # Your Lambda function code and dependencies
    handler="retrain_handler.handler", # The function entrypoint in your Python file
    role=lambda_execution_role.arn)

# Define the notification for the bucket to trigger the lambda function
bucket_notification = aws.s3.BucketNotification("bucketNotification",
    bucket=training_data_bucket.id,
    lambda_functions=[aws.s3.BucketNotificationLambdaFunctionArgs(
        lambda_function_arn=ml_model_retraining_lambda.arn,
        events=["s3:ObjectCreated:*"],
        filter_prefix="data/", # Assuming new data is uploaded to the 'data/' prefix
    )])

# Use pulumi's 'depends_on' to ensure lambda permission is created after the lambda function
lambda_permission = aws.lambda_.Permission("lambdaPermission",
    action="lambda:InvokeFunction",
    function=ml_model_retraining_lambda.name,
    principal="s3.amazonaws.com",
    source_arn=training_data_bucket.arn,
    depends_on=[ml_model_retraining_lambda])

# Export the S3 bucket name and Lambda Function ARN
pulumi.export('bucket_name', training_data_bucket.id)
pulumi.export('lambda_function_arn', ml_model_retraining_lambda.arn)
```

The above Pulumi program does the following:

- Defines an S3 bucket where training data files will be stored.
- Creates an IAM role with the necessary policies for the Lambda function, allowing it to be invoked by S3 and log to CloudWatch Logs.
- Sets up the Lambda function with your model retraining code (you'll need to provide the Python code for `retrain_handler.handler` inside the `./lambda` directory).
- Configures S3 bucket notifications to trigger the Lambda function when new data is added with the prefix `data/`.
- Grants the S3 service permission to invoke the Lambda function.
- Exports the S3 bucket name and Lambda function ARN for further references outside Pulumi.

Replace the placeholder `./lambda` with the path to your Lambda function code, which should have a file with a function `handler` that will be invoked on each event.

Please ensure that you have AWS CLI configured with the necessary permissions and Pulumi CLI installed to execute this program.