Using Aws Apigatewayv2 With Sagemaker

In this solution, we will create an AWS API Gateway V2 and integrate it with an AWS SageMaker endpoint using Pulumi in TypeScript. This setup allows us to expose a SageMaker machine learning model as a RESTful API endpoint, enabling easy access and interaction with the model.

Introduction

In this solution, we will use AWS API Gateway V2 to create a RESTful API that integrates with an AWS SageMaker endpoint. This setup allows us to expose a SageMaker machine learning model as an API, making it accessible for various applications. The key services involved in this solution are AWS API Gateway V2 and AWS SageMaker.

Step-by-Step Explanation

Step 1: Create a SageMaker Model Endpoint

• Define the SageMaker model using the appropriate model data and container image.
• Create a SageMaker endpoint configuration.
• Deploy the SageMaker endpoint.

Step 2: Create an API Gateway V2

• Define the API Gateway V2 with the necessary routes and integrations.
• Integrate the API Gateway with the SageMaker endpoint using a VPC link or direct integration.

Step 3: Deploy the Infrastructure

• Use Pulumi to deploy the defined infrastructure.
• Verify the deployment and test the API endpoint.

Key Points

• AWS API Gateway V2 provides a scalable and secure way to expose RESTful APIs.
• AWS SageMaker allows you to build, train, and deploy machine learning models at scale.
• Integrating API Gateway with SageMaker enables easy access to machine learning models via API endpoints.
• Pulumi allows for infrastructure as code, making the deployment process repeatable and manageable.

Conclusion

By following this solution, you can successfully create an API Gateway V2 that integrates with a SageMaker endpoint using Pulumi in TypeScript. This setup provides a scalable and secure way to expose machine learning models as RESTful APIs, enabling easy access and interaction with the models.

Full Code Example

import * as pulumi from "@pulumi/pulumi";
import * as aws from "@pulumi/aws";

// Step 1: Create a SageMaker Model Endpoint
const modelEndpoint = new aws.sagemaker.Endpoint("modelEndpoint", {
    endpointConfigName: "my-endpoint-config",
    name: "my-sagemaker-endpoint",
});

// Step 2: Create an API Gateway V2
const api = new aws.apigatewayv2.Api("api", {
    protocolType: "HTTP",
    name: "sagemaker-api",
});

// Create an integration with the SageMaker endpoint
const integration = new aws.apigatewayv2.Integration("integration", {
    apiId: api.id,
    integrationType: "HTTP_PROXY",
    integrationUri: modelEndpoint.arn,
    integrationMethod: "POST",
    payloadFormatVersion: "1.0",
});

// Create a route for the API
const route = new aws.apigatewayv2.Route("route", {
    apiId: api.id,
    routeKey: "POST /predict",
    target: pulumi.interpolate`integrations/${integration.id}`,
});

// Create a stage for the API
const stage = new aws.apigatewayv2.Stage("stage", {
    apiId: api.id,
    name: "$default",
    autoDeploy: true,
});

// Export the API endpoint
export const apiEndpoint = pulumi.interpolate`${api.apiEndpoint}`;