Heart Disease Prediction MLOps Pipeline

This repository contains an end-to-end MLOps project for predicting heart disease risk. It demonstrates a full machine learning lifecycle including data acquisition, experimentation, containerization, and deployment on Kubernetes.

Video link - https://youtu.be/6a1YchafXDw

📄 Read the Full Project Report for detailed architecture, modeling choices, and experiments.

🚀 Key Features

• Automated Data Pipeline: Fetches and processes data from the UCI Repository.
• Experiment Tracking: Uses MLflow to log parameters, metrics, and model artifacts.
• Containerization: Docker image with a production-ready FastAPI application.
• Deployment: Kubernetes manifests for high-availability deployment with LoadBalancer.
• Monitoring: Integrated Prometheus metrics and Grafana dashboard support.
• CI/CD: GitHub Actions pipeline for automated linting, testing, and artifact generation.

🛠️ Quick Start

1. Install Dependencies

python -m venv .venv
.\.venv\Scripts\Activate.ps1   # Windows
# source .venv/bin/activate    # Linux/Mac
pip install -r requirements.txt

2. Run Training

python src/data_loader.py
python src/train.py --model_type logistic_regression
python src/train.py --model_type random_forest

3. Run API Locally (Docker)

docker build -t mlops-heart-disease:latest .
docker run -p 8000:8000 mlops-heart-disease:latest

Access Swagger UI at: http://localhost:8000/docs

4. Deploy to Kubernetes

kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/monitoring.yaml
kubectl apply -f k8s/service.yaml

📂 Project Structure

• src/ - Source code for API, training, and data loading.
• tests/ - Unit tests for the application.
• k8s/ - Kubernetes deployment and monitoring manifests.
• notebooks/ - Jupyter notebooks for EDA.
• .github/workflows/ - CI/CD pipeline configuration.

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Created by Yash Verma for MLOps Assignment 1.