Building a Modern AI-Powered Web App with Django, Wagtail, and Machine Learning

Introduction

Modern web apps are increasingly powered by AI. In this post, we'll walk through building a scalable, maintainable web app using Django, Wagtail, and machine learning integration.

Project Architecture

- Django for backend logic and APIs - Wagtail for CMS and content management - scikit-learn or TensorFlow for ML - Docker for deployment - AWS SSM for secrets management

Step 1: Setting Up Django and Wagtail

Start by creating a new Django project and adding Wagtail. Configure your settings for modularity and security.

django-admin startproject myproject
cd myproject
pip install wagtail
wagtail start mysite

Step 2: Integrating Machine Learning

Train your ML model (e.g., with scikit-learn), save it with joblib, and load it in your Django app for predictions.

import joblib
model = joblib.load("model.pkl")
prediction = model.predict([[1, 2, 3]])

Step 3: Building the Frontend

Use Wagtail's StreamField for flexible content, and Django templates for dynamic UI. Add forms for user input and display ML results interactively.

Step 4: Deployment and Scaling

Containerize your app with Docker, use AWS SSM for secrets, and deploy to a scalable cloud platform.

FROM python:3.12
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["gunicorn", "mysite.wsgi"]

Conclusion

With Django, Wagtail, and ML, you can build powerful, intelligent web apps that scale. The key is modular architecture, robust deployment, and continuous improvement.