Secured Access to Machine Learning Models via API Gateway

Question

Pulumi · Accepted Answer

To provide secured access to Machine Learning models via an API Gateway, you can set up an AWS API Gateway with Lambda proxy integration that can invoke a Lambda function hosting your ML model. You can then secure the API Gateway using API keys, IAM roles, Lambda authorizers, or Cognito user pools based on your security requirements.

Here's a step-by-step guide followed by the Pulumi program written in Python:

### Step 1: Define a Lambda function
You need a Lambda function that loads your ML model and processes incoming requests. The Lambda function needs sufficient permissions and roles to execute and access resources that it needs.

### Step 2: Create an API Gateway
Once you have your Lambda function, create a REST API Gateway that will route incoming requests to your Lambda function. Use API Gateway features like resources, methods, and integrations to set this up.

### Step 3: Secure the API Gateway
After creating the API Gateway, secure it with API keys or other appropriate authentication mechanisms to ensure only authorized users can access your ML models.

### Step 4: Deploy the API Gateway
Deploy the API Gateway to make it accessible over the internet. After deploying, you'll get an endpoint URL that you can share with your users.

### Step 5: Export the necessary outputs
Export the endpoint URL and any other necessary information from your Pulumi stack to interact with the deployed API.

Let's write the Pulumi program for this setup:

```python
import pulumi
import pulumi_aws as aws

# Step 1: Define a Lambda function
# Replace 'my_ml_model_handler' with the actual handler for your ML model
ml_lambda_role = aws.iam.Role("mlLambdaRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            }
        }]
    }"""
)

# Attach the AWS Lambda Basic Execution Role to your Lambda function
lambda_basic_execution_attachment = aws.iam.RolePolicyAttachment(
    "lambdaBasicExecutionAttachment",
    role=ml_lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
)
 
# Upload your code to an S3 bucket for Lambda to access
lambda_code_bucket = aws.s3.Bucket('lambda-code-bucket')

lambda_code = aws.s3.BucketObject('ml-model-lambda-code',
    bucket=lambda_code_bucket.id,
    key='ml_model.zip',
    source=pulumi.FileAsset('path/to/ml_model.zip')
)

ml_model_lambda = aws.lambda_.Function("mlModelLambda",
    role=ml_lambda_role.arn,
    handler="my_ml_model_handler",
    runtime="python3.8",
    code=lambda_code.bucket.apply(lambda bucket: f"s3://{bucket}/{lambda_code.key}")
)

# Step 2: Create an API Gateway
api_gateway = aws.apigateway.RestApi('apiGateway',
    description='API Gateway for ML model'
)

# Resource representing the ML Model endpoint
ml_model_resource = aws.apigateway.Resource('mlModelResource',
    rest_api=api_gateway.id,
    parent_id=api_gateway.root_resource_id,
    path_part='mymodel'
)

# POST method for ML Model
ml_model_post_method = aws.apigateway.Method('mlModelPostMethod',
    rest_api=api_gateway.id,
    resource_id=ml_model_resource.id,
    http_method='POST',
    authorization='NONE'
)

# Lambda integration
ml_model_lambda_integration = aws.apigateway.Integration('mlModelLambdaIntegration',
    rest_api=api_gateway.id,
    resource_id=ml_model_resource.id,
    http_method=ml_model_post_method.http_method,
    integration_http_method='POST',
    type='AWS_PROXY',
    uri=ml_model_lambda.invoke_arn
)

# Step 3: Deploy the API Gateway
deployment = aws.apigateway.Deployment('apiGatewayDeployment',
    rest_api=api_gateway.id,
    # Note: to update the deployment with changes, we must create a new resource
    stage_name='prod'
)

# Step 4: Export the necessary outputs
api_gateway_endpoint = pulumi.Output.concat("https://", api_gateway.id, ".execute-api.", pulumi.get_region(), ".amazonaws.com/prod/mymodel")

pulumi.export("api_gateway_endpoint", api_gateway_endpoint)
```

Remember to replace `'path/to/ml_model.zip'` with the actual path to the zipped code of your ML model. Also, the handler name `'my_ml_model_handler'` should be the function within your code that AWS Lambda will call to execute your ML model.

This program creates the necessary AWS resources to securely expose your ML model through an API Gateway. The `AWS_PROXY` integration directly passes the request to the Lambda function and gets the response back from Lambda, acting as a proxy. The endpoint URL which you can use to access the ML model is provided as an output from the Pulumi stack.