1. Secure Model Deployment with Auth0


    Deploying a machine learning model securely involves setting up an API that will serve model predictions. You need to ensure only authorized users or applications can access it. Auth0 provides robust authentication and authorization services and is a common choice for managing access to APIs.

    Here's how you could use Pulumi to create a serverless function to serve a machine learning model, protect it with Auth0, and deploy it to a cloud provider, like AWS, Azure, or GCP. For this example, we will use AWS Lambda and API Gateway with Auth0 authorization.

    Explanation & Setup

    To begin, you will need to have an Auth0 account and an Auth0 API defined that represents your machine learning model service. Auth0 APIs allow you to define the expected authorization characteristics for your custom API, and this is what we'll use in the AWS API Gateway for authorization validation.

    The high-level steps we'll cover include:

    1. Creating an Auth0 API.
    2. Defining AWS Lambda that serves the machine learning model.
    3. Setting up AWS API Gateway to manage access to the Lambda function.
    4. Configuring Auth0 as the authorizer for the API Gateway.

    In Pulumi, you declare infrastructure in code, which allows you to perform all these steps programmatically and similarly across cloud providers.

    Let's start with the Pulumi program:

    import pulumi import pulumi_aws as aws import pulumi_auth0 as auth0 # Replace these values with your Auth0 domain and API identifier auth0_domain = "your_auth0_domain" auth0_api_identifier = "your_auth0_api_identifier" # Create the Auth0 API that represents your machine learning model service auth0_api = auth0.Api("machine-learning-api", identifier=auth0_api_identifier, scopes=[auth0.ApiScopeArgs( description="Predict", value="predict", )] ) # Create an AWS IAM role that can be assumed by Auth0 auth_role = aws.iam.Role("auth0-auth-role", assume_role_policy=f'''{{ "Version": "2012-10-17", "Statement": [{{ "Effect": "Allow", "Principal": {{ "Federated": "arn:aws:iam::{aws.get_caller_identity().account_id}:oidc-provider/{auth0_domain}/" }}, "Action": "sts:AssumeRoleWithWebIdentity", "Condition": {{ "StringEquals": {{ "{auth0_domain}/:sub": "YOUR_AUTH0_CLIENT_ID" }} }} }}] }}''') # Attach the necessary policy to the role policy_attach = aws.iam.RolePolicyAttachment("role-policy-attachment", role=auth_role.id, policy_arn=aws.iam.ManagedPolicy.AWSLambdaBasicExecutionRole ) # Assume API Gateway will somehow validate the token (details omitted). # Define an AWS Lambda function to process the requests lambda_function = aws.lambda_.Function("model-serving-fn", handler="index.handler", role=auth_role.arn, runtime="python3.8", code=pulumi.AssetArchive({ ".": pulumi.FileArchive("./path_to_your_function_code") }), ) # Set up AWS API Gateway to manage access to the lambda function api_gateway_rest_api = aws.apigateway.RestApi("ml-model-api", description="API for ML Model" ) api_gateway_resource = aws.apigateway.Resource("ml-model-resource", parent_id=api_gateway_rest_api.root_resource_id, path_part="predict", rest_api=api_gateway_rest_api.id, ) api_gateway_method = aws.apigateway.Method("ml-model-method", http_method="POST", authorization="CUSTOM", # The "authorizer_id" can be set to the ID of an AWS API Gateway Authorizer # which provides a way to manage Auth0 access token validation. authorizer_id="YOUR_AUTHORIZER_ID", resource_id=api_gateway_resource.id, rest_api=api_gateway_rest_api.id, ) api_gateway_integration = aws.apigateway.Integration("ml-model-integration", http_method=api_gateway_method.http_method, integration_http_method="POST", type="AWS_PROXY", uri=lambda_function.invoke_arn, resource_id=api_gateway_resource.id, rest_api=api_gateway_rest_api.id, ) lambda_permission = aws.lambda_.Permission("api-gateway-lambda-permission", action="lambda:InvokeFunction", function=lambda_function.name, principal="apigateway.amazonaws.com", source_arn=api_gateway_method.execution_arn, ) # Export the API URL pulumi.export("api_url", api_gateway_rest_api.execution_arn.apply( lambda arn: f"https://{arn}.execute-api.{aws.config.region}.amazonaws.com/prod/predict" ))

    Explanation of the Pulumi Program

    This program does the following:

    1. Auth0 API Resource: It creates an Auth0 API that defines the predict scope.

    2. AWS IAM Role: An AWS IAM role is created that Auth0 will assume to authorize requests. The role's trust relationship allows the sts:AssumeRoleWithWebIdentity action for Auth0.

    3. AWS Lambda Function: The function named model-serving-fn will be invoked by API Gateway to serve model predictions. The code for this function is read from a local directory containing your Python model serving code.

    4. API Gateway: We set up a REST API with an /predict endpoint. The endpoint accepts POST requests and is integrated with our Lambda function.

    5. API Gateway Authorization: The API method uses a custom authorizer (not created in this snippet) to validate the Auth0 tokens. An AWS API Gateway Authorizer needs to be set up separately to handle this. You would typically use something like a Lambda function to perform the necessary validation calls to Auth0 within the custom authorizer.

    6. Lambda Permission: Grants API Gateway permissions to invoke the Lambda function.

    7. Exports: The last line exports the endpoint URL to be used by clients to make prediction requests.

    Remember, this example assumes you have some machine learning model serving code in a local directory set up to be used as an AWS Lambda function. Also, the custom authorizer that integrates Auth0 with AWS API Gateway needs to be created separately and linked to the API Gateway method via its ID.

    The AWS Lambda function would be where you load and serve your machine learning model. It could be a Python function that loads the model upon initialization and provides an HTTP endpoint (via API Gateway) to make predictions based on the model.

    Remember to replace placeholders like your_auth0_domain, your_auth0_api_identifier, and YOUR_AUTHORIZER_ID with actual values from your setup. This is crucial for the security and functionality of your deployment. The IAM role's trust relationship is particularly sensitive as it controls who can assume the role based on the Auth0 token's subject claim.