End-to-End MLOps Pipeline – Student Performance Prediction

This project implements a production-ready end-to-end ML pipeline for predicting student performance using structured educational data. The system follows full MLOps best practices, covering the entire ML lifecycle from development to deployment.

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🚀 Key Features

• Modular ML pipeline (ingestion → validation → transformation → training → evaluation)
• DVC for data & model versioning
• MLflow + DagsHub for experiment tracking and model registry
• Dockerized training & deployment
• AWS SageMaker for cloud-based training and inference
• Reproducible experiments & environment management

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🛠 Tech Stack

Python, Scikit-learn, Pandas, MLflow, DVC, DagsHub, Docker, AWS SageMaker

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📌 Use Case

Predict student math, reading, and writing performance from demographic and academic features using a scalable and reproducible ML system.

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▶️ Run Project

dvc pull
pip install -r requirements.txt
python app.py