Malik Abualzait

Posted on Jan 11

Deploying AI Agents with AWS, Kubernetes, and Python's FastAPI Powerhouse

#ai #tech #programming #tutorial

Building and Deploying AI Agents on Kubernetes with AWS Bedrock, FastAPI, and Helm

In this tutorial, we'll explore how to create, deploy, and run an AI model that provides REST APIs for summarization and translation using AWS Bedrock, FastAPI, Docker, and deployment on Amazon EKS via Helm. By following these steps, you'll be able to integrate AI into your operations with a reusable process.

What are AI Agents?

AI agents are lightweight, specialized AI models that can be managed, scaled, and deployed like microservices in a cloud-native environment. They're designed for specific tasks such as summarization, translation, classification, or other analytical tasks.

Step 1: Set Up AWS Bedrock

AWS Bedrock is an open-source framework that provides a scalable and secure way to deploy AI models on Kubernetes. To set up AWS Bedrock, follow these steps:

Install the required dependencies using pip:

pip install aws-bedrock fastapi uvicorn docker

Create a new directory for your project and navigate into it:
```
mkdir ai-agent-project
cd ai-agent-project
```
Initialize a new Python package using cookiecutter:
```
cookiecutter https://github.com/aws/bedrock.git
```

Step 2: Implement AI Model with FastAPI

In this step, we'll implement the AI model using FastAPI. Create a new file called main.py and add the following code:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Text(BaseModel):
    text: str

@app.post("/summarize")
async def summarize(text: Text):
    # Implement AI model for summarization here
    summary = "This is a summarized version of the input text."
    return {"summary": summary}

@app.post("/translate")
async def translate(text: Text):
    # Implement AI model for translation here
    translation = "This is a translated version of the input text."
    return {"translation": translation}

Step 3: Containerize with Docker

In this step, we'll containerize our application using Docker. Create a new file called Dockerfile and add the following code:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .

RUN pip install -r requirements.txt

COPY . .

CMD ["uvicorn", "main:app --host 0.0.0.0 --port 8000"]

Step 4: Deploy on Amazon EKS with Helm

In this step, we'll deploy our application on Amazon EKS using Helm. Create a new file called values.yaml and add the following code:

replicaCount: 1

image:
  repository: <your-docker-image-repo>
  tag: <your-docker-image-tag>

service:
  type: NodePort

Step 5: Run Helm Chart

In this final step, we'll run the Helm chart to deploy our application on Amazon EKS. Create a new file called Chart.yaml and add the following code:

apiVersion: v2
appVersion: "1.0"
description: A Helm chart for deploying AI agents on Kubernetes

name: ai-agent-chart

type: application

version: 1.0.0

Run the following command to deploy your application:

helm install --set image.repository=<your-docker-image-repo> --set image.tag=<your-docker-image-tag> .

Conclusion

In this tutorial, we've covered how to create, deploy, and run an AI model that provides REST APIs for summarization and translation using AWS Bedrock, FastAPI, Docker, and deployment on Amazon EKS via Helm. By following these steps, you'll be able to integrate AI into your operations with a reusable process.

Best Practices

Use a lightweight framework like FastAPI for building the AI model.
Containerize your application using Docker to ensure consistency across environments.
Deploy on Kubernetes using Helm to take advantage of its scalability and security features.

By following these best practices, you'll be able to build and deploy reliable AI agents that meet the needs of your operations.

By Malik Abualzait

DEV Community