Overview

An earlier machine learning deployment project for house price prediction, built as a Flask application with an AWS deployment workflow and automated GitHub Actions pipeline.

Architecture

  • Flask application — serves the prediction interface and inference flow
  • AWS ECR — stores the Docker image built by the CI pipeline
  • AWS EC2 — hosts the deployed container and self-hosted GitHub runner
  • GitHub Actions — runs CI and pushes the deployment image

Features

  • Interactive prediction UI — users can change housing features and see updated predictions
  • Containerized deployment workflow — Docker image built and pushed through GitHub Actions
  • End-to-end project structure — data ingestion, transformation, training, and prediction pipelines in one codebase

Tech Stack

  • Python (Scikit-learn, Pandas, Flask)
  • AWS (ECR, EC2)
  • GitHub Actions
  • Docker

Key Takeaway

This project was an early end-to-end ML deployment exercise focused on packaging a prediction model as a containerized web app with an AWS deployment workflow and CI/CD automation.