Bird Disease Prediction - End-to-End MLOps (Deep Learning)

Built an end-to-end MLOps deep learning project that predicts bird disease classes from images and packages the full workflow from experimentation to deployment. The pipeline covers data ingestion and preprocessing, model training (CNN/transfer learning), evaluation with standard metrics, and versioned model packaging for reproducible releases.

The project is structured like a production system: modular pipeline stages, configurable environments, and deployment-ready components (API/app + containerization). It’s designed to be extended with CI/CD automation, model registry/versioning, and monitoring patterns typically used in real MLOps teams.