Turn Your AI Model into a Real Product (Amazon SageMaker, API Gateway, AWS Lambda, Next.js, Python)

AWSArtificial IntelligenceMachine Learning

Sep 5

Transforming an Amazon SageMaker AI model, like Meta’s ultra-powerful LLaMA 2 LLM model, into a tangible, user-friendly product marks a significant leap from theory to practice in the realm of machine learning and artificial intelligence. This journey entails not just the deployment of models, their deployments, the DevOps requirements to configure the servers, and all the IAM configurations (the list just goes on and on), but also their integration into applications that can be interactively used by end-users on the web. The process involves a blend of MLOps (Machine Learning Operations) practices and cloud technologies, ensuring your AI model is not just a piece of code that lives in a notebook, but a part of a scalable, efficient, and accessible product that real users in the real world can use.

1. Building an API with Amazon API Gateway

Why an API is Key: An API (Application Programming Interface) acts as the bridge between the AI model (in this case, Meta’s LLaMA 2) and the applications that use it (in this case, a Next.js app with TypeScript). It allows your model to communicate with other software applications seamlessly. By utilizing Amazon API Gateway, you can create, publish, maintain, monitor, and secure APIs at any scale. This service provides a robust, scalable platform to make your SageMaker model accessible over the internet.

Step-by-Step Guide:

Deploy Your Model on SageMaker: Ensure your model is deployed and hosted on Amazon SageMaker, making it ready to accept API calls.
Set Up API Gateway: Use Amazon API Gateway to create a new API. This will serve as the front door for applications to access your model's predictions.
Configure Endpoints: Define resources and methods in your API that correspond to different functions of your AI model. For instance, you could have a POST method to send data for prediction.
Security and Throttling: Implement necessary security measures, such as API keys and authorization, to protect your API. Additionally, configure throttling to manage the load on your model.

2. Writing a Serverless Function using AWS Lambda

Leveraging AWS Lambda: AWS Lambda allows you to run code without provisioning or managing servers. This serverless compute service can execute your code in response to HTTP requests via Amazon API Gateway or other AWS services.

Integration with SageMaker:

Create a Lambda Function: Write a function in AWS Lambda that invokes your SageMaker model endpoint. This function can be triggered by API Gateway, enabling a serverless architecture for your application.
Handling Requests and Responses: Your Lambda function should parse incoming requests, format them for your model, and then send them to the SageMaker endpoint. It should also process the response from SageMaker and format it for the client application.

3. Writing a Script in Python

Python Script for Direct Interaction:

Utilize Boto3 SDK: Write a Python script that uses the Boto3 SDK to interact directly with your SageMaker endpoint. This script can be used for batch processing or integrating your model into other Python applications.
Script Functionality: Ensure your script can handle data preprocessing, invoking the SageMaker endpoint, and post-processing of the model's predictions.

4. Building an App using Next.js, TypeScript, and Tailwind CSS

Creating a User-Friendly Interface:

Frontend Development: Use Next.js, a React framework, for building your application's user interface. TypeScript adds type safety, enhancing code quality and maintainability. Tailwind CSS can be used for styling, offering a utility-first approach for a responsive design.
Integration with Your API: Make your application interact with the SageMaker model via the API you set up with API Gateway. Use Axios or Fetch API for making HTTP requests from your Next.js application.

5. Final Thoughts

Transforming your AI model into a real product involves more than just technical implementation. It requires a strategic approach to design, user experience, and scalability. By following these steps and leveraging AWS services like SageMaker, API Gateway, Lambda, and utilizing frameworks like Next.js, you can build robust, scalable, and user-friendly AI-powered applications. This journey from model deployment to product realization is not just about making your model accessible but also about creating value for your users, offering them tangible, impactful solutions powered by artificial intelligence.

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In this video, we will continue our MLOps series by highlighting 4 specific patterns for building AI products by connecting to our deployed Amazon SageMaker endpoint. If you’re like me, the question about machine learning once you deploy a model is: “how do we get our model outside of the Juypter Notebook?” Whether you’re deploying the model to Amazon SageMaker, Google CoLab, or some other hosting platform, I have felt that it has been rather confusing or nebulous at best to understand how to get the model “out of the cloud” and into a real application that you or your users could use.

If you’re wondering about this too, then this video is for you!

We are going to go show how to call a deployed model, LLaMA 2 which is a Large Language Model, in a variety of different ways outside of Amazon SageMaker. We will even build our own LLaMA Chat application (a ChatGPT clone) to show you how a real application could connect directly to a model.

Here’s what we’ll go through:

(1) 🔊 Build an API with Amazon API Gateway

(2) ☁️ Write a Serverless Function using AWS Lambda

(3) 🐍 Write a Script in Python

(4) 📲 Build an App using Next.js, TypeScript, and Tailwind CSS

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