Aws

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 Links GitHub Repository 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. ...