Artificial Intelligence Cloud Infrastructure

AWS AI Stack – Build Scalable, Serverless AI Applications with AWS

October 5, 2024

TL;DR

This guide will show you how to use the AWS AI Stack and Serverless Framework to build serverless AI applications with ease. You’ll learn how to install npm, deploy your app, and use powerful AWS services like Lambda, API Gateway, and Bedrock AI models to create AI chatbots, handle authentication, and manage backend logic. You’ll also see real examples and outputs for each step, making it simple to get started.

Installing npm and Getting Started

Before jumping into the AWS AI Stack and Serverless Framework, you need to install npm (Node Package Manager), which is necessary for managing JavaScript dependencies and running the Serverless Framework.

Step 1: Install npm

If you don’t have npm installed, here’s how to do it:

  1. Install Node.js (npm comes with Node.js): Go to the Node.js website and download the latest version for your operating system.
  2. Verify Installation: Once installed, open your terminal (or command prompt) and run:bashCopy codenode -v npm -v This should output the version numbers of both Node.js and npm, confirming the installation.

Step 2: Install the Serverless Framework

Now that you have npm, you can install the Serverless Framework. This tool helps you build and deploy serverless applications to AWS.

Run the following command in your terminal:

npm install -g serverless

This will globally install the Serverless Framework on your machine.

Examples of Using the AWS AI Stack

Once you have npm and the Serverless Framework installed, you can start building your AI application using the AWS AI Stack.

Here are some examples to help you get started:

Example 1: Deploy an AI Chatbot Using AWS AI Stack

  1. Set up your AWS credentials: Make sure your AWS credentials are configured. If not, run:bashCopy codeaws configure This will prompt you to enter your AWS access key, secret key, and region.
  2. Download the AWS AI Stack: Clone the AWS AI Stack project from GitHub or create your own project folder.
  3. Install dependencies: Navigate to your project folder and run:bashCopy codenpm install This installs all the required dependencies for the AI Stack, including libraries for Lambda functions and AI model interactions.
  4. Deploy your application: Now, deploy the services using the Serverless Framework:bashCopy codeserverless deploy

Output: When you deploy the AI stack, the Serverless Framework will provide output similar to this:

Deploying "ai-chat-app" to stage "dev" (us-east-1)

Service Information
service: ai-chat-app
stage: dev
region: us-east-1
stack: ai-chat-app-dev
resources: 12
api keys:
  None
endpoints:
  POST - https://xyz123.execute-api.us-east-1.amazonaws.com/chat
functions:
  chat: ai-chat-app-dev-chat

This output shows the endpoints, services, and Lambda functions created in AWS. The AI chatbot can now be accessed via the provided endpoint.

Example 2: Set Up Authentication Using JWT Tokens

This AI stack also supports authentication using JWT tokens. Here’s how to implement it:

  1. Create authentication services: Define your authentication Lambda function in the serverless.yml file:yamlCopy codefunctions: login: handler: auth.login events: - http: path: auth/login method: post register: handler: auth.register events: - http: path: auth/register method: post
  2. Deploy the authentication service: Run the following command to deploy the authentication service:bashCopy codeserverless deploy -f auth

Output:

Deploying function "auth" to stage "dev" (us-east-1)

Service Information
service: auth
stage: dev
region: us-east-1
endpoints:
  POST - https://xyz123.execute-api.us-east-1.amazonaws.com/auth/login
  POST - https://xyz123.execute-api.us-east-1.amazonaws.com/auth/register
functions:
  auth: auth-dev-auth

Now, users can log in and register by making POST requests to these endpoints, and JWT tokens will be issued for authentication.

Working Locally for Development

During development, you might want to test your services locally before deploying. The Serverless Framework allows you to run services locally to speed up your workflow.

Example 3: Run a Lambda Function Locally

To run your AI chatbot function locally:

  1. Run the chatbot function in dev mode:bashCopy codeserverless invoke local --function chat
  2. Test locally: You can send a local test request to your function:bashCopy codecurl -X POST "http://localhost:3000/chat" -d '{"message": "Hello, AI!"}'

Output:

code{
  "response": "Hi there! How can I assist you today?"
}

This output shows the AI chatbot responding to the test message. You can continue making changes and testing your function locally before deploying it to AWS.

Conclusion

The AWS AI Stack combined with the Serverless Framework provides a powerful, scalable, and cost-effective solution for building AI-driven applications. By following the examples above, you can easily deploy AI chatbots, implement authentication, and scale your app with serverless architecture—all while paying only for the resources you use.

Whether you’re just starting out or looking to build complex AI apps, the AWS AI Stack and Serverless Framework offer an easy entry point to cloud-based AI development.

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