ML Portfolio Website

This site serves as a full-stack showcase of machine learning deployments and interactive demos.

View on GitHub

Project Overview

This portfolio website was built from scratch using Flask (Python web framework), Jinja2 templating, and HTML/CSS. It is deployed on Render and integrates two live machine learning demos:

  • A SageMaker-hosted Iris Classifier with real-time prediction
  • A GCP-hosted Transformer model for English to French/German translation

The site also features attention visualizations, custom JS for dynamic interactivity, and modular routing for easy expansion.

Technologies Used

  • Frontend: HTML, CSS, Jinja2, Plotly.js
  • Backend: Python, Flask
  • ML Frameworks: PyTorch, scikit-learn
  • Deployment: Render (Web), AWS SageMaker (Iris), GCP (Transformer API)