End-to-End MLOps Classification (AWS + FastAPI + CI/CD)

Built an end-to-end MLOps pipeline for a U.S. Visa Approval Classification System, designed to take a model from data ingestion → training → deployment → monitoring with production-grade practices. The project covers data ingestion and transformation, model training with hyperparameter optimization, and a repeatable pipeline structure for scalability and maintainability. It also supports training multiple models (e.g., XGBoost/CatBoost/RandomForest) and persisting artifacts for reproducible runs. GitHub

For deployment, the application is served via FastAPI (with a simple Jinja2 web UI and REST prediction endpoint), containerized with Docker, and deployed on AWS EC2. The Docker image is stored in AWS ECR, and deployments are automated through a GitHub Actions CI/CD pipeline. The project additionally includes model registry/versioning using AWS S3 and continuous evaluation/drift monitoring with Evidently AI, enabling ongoing reliability checks as data changes over time