Serverless API Backends for AI Model Inferences

Question

Pulumi · Accepted Answer

Serverless backends are often used for deploying AI model inferences due to their ability to scale automatically with the number of inference requests and their cost-effectiveness for intermittent or unpredictable workloads.

To set up a serverless API backend for AI model inferences, one can use various cloud provider services. Depending on the provider and specific requirements, different resources and services will be used.

For AWS, we can utilize Amazon SageMaker and AWS Lambda:
- **Amazon SageMaker Endpoint**: You can deploy trained machine learning models to SageMaker Endpoints, making them accessible for real-time inferences.
- **AWS Lambda**: Lambda functions can be used to handle API requests and invoke the SageMaker Endpoint to perform inferences.

For Azure, we would be looking into Azure Machine Learning and serverless options:
- **Azure Machine Learning Services**: Azure provides the ability to deploy machine learning models as inference endpoints which can be consumed by API backends.
- **Azure Functions**: Similar to AWS Lambda, Azure Functions provide serverless compute that can be used to host APIs and interact with Azure ML model endpoints.

In the example below, I'll demonstrate how to set up a serverless API backend using AWS services. We'll create an AWS Lambda function that will serve as the API endpoint. This function will be responsible for invoking a SageMaker Endpoint to perform model inferences.

```python
import pulumi
import pulumi_aws as aws

# Assume the SageMaker model and endpoint configuration are already in place and we have their ARNs.
# Replace `sagemaker_model_arn` and `sagemaker_endpoint_config_arn` with the actual ARNs from your AWS setup.
sagemaker_model_arn = "arn:aws:sagemaker:us-east-1:123456789012:model/example-model"
sagemaker_endpoint_config_arn = "arn:aws:sagemaker:us-east-1:123456789012:endpoint-config/example-config"

# Create SageMaker Endpoint
sagemaker_endpoint = aws.sagemaker.Endpoint("MyModelEndpoint",
    endpoint_config_name=sagemaker_endpoint_config_arn,
    tags={
        "Purpose": "AIModelInference"
    }
)

# IAM role to allow Lambda to access SageMaker and CloudWatch Logs
lambda_role = aws.iam.Role("aiInferenceLambdaRole",
    assume_role_policy=json.dumps({
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            },
        }]
    }),
    tags={
        "Purpose": "LambdaExecutionRole"
    }
)

# Attach policies to the IAM role created for Lambda function
lambda_policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment",
    role=lambda_role.id,
    policy_arn=aws.iam.ManagedPolicies.AWSLambdaBasicExecutionRole
)

sagemaker_policy_attachment = aws.iam.RolePolicyAttachment("sagemakerPolicyAttachment",
    role=lambda_role.id,
    policy_arn=aws.iam.ManagedPolicies.AmazonSageMakerFullAccess
)

# Lambda Function
ai_inference_function = aws.lambda_.Function("AIInferenceFunction",
    runtime=aws.lambda_.Runtime.PYTHON_3_8,
    code=pulumi.AssetArchive({
        ".": pulumi.FileArchive("./lambda")
    }),
    handler="handler.endpoint_handler",  # Your handler file and function
    role=lambda_role.arn,
    timeout=30,
    environment={
        "variables": {
            "SAGEMAKER_ENDPOINT_NAME": sagemaker_endpoint.endpoint_name
        }
    },
    tags={
        "Purpose": "AIModelInference"
    }
)

# Define API Gateway to trigger the Lambda Function
api_gateway = aws.apigatewayv2.Api("aiInferenceApi",
    protocol_type="HTTP",
    route_selection_expression="${request.method} ${request.path}",
    target=ai_inference_function.arn,  # Target the Lambda function ARN
    tags={
        "Purpose": "ServerlessAIBackend"
    }
)

# Export the HTTP API URL for client applications to use
pulumi.export("api_endpoint", api_gateway.api_endpoint)
```

In this code, we begin by setting up a Lambda function that will serve our API requests. We provide it with the runtime (Python 3.8), the source code location, and the handler information. The environment variable `SAGEMAKER_ENDPOINT_NAME` is set to the name of the SageMaker endpoint from which it will retrieve inference results.

As for the API Gateway configuration, we create an HTTP API that triggers the Lambda function. This is a serverless and scalable way to expose our inference endpoint to clients. We export the API Gateway endpoint so that you can use it in your client application to send inference requests.

Remember to include any required permissions to your Lambda function's IAM role so that it can call SageMaker and log to CloudWatch Logs, as well as any other services it might need to interact with. This is done through attaching managed policies, such as `AWSLambdaBasicExecutionRole` for logging and `AmazonSageMakerFullAccess` for interacting with SageMaker.

Please make sure to replace placeholders such as the SageMaker model and endpoint configuration ARNs with actual values. Additionally, the Lambda source code directory (`"./lambda"`) should contain your function code, organized properly with all its dependencies.

You would run this Pulumi program by saving it to a file and using the Pulumi CLI to deploy it using `pulumi up` in your terminal. Make sure you have AWS credentials configured for Pulumi to use.