MLOps Pipeline for Iris Classification

This project demonstrates a complete MLOps pipeline using the Iris dataset for flower species classification.

🏗️ Architecture

• Data: Iris dataset (classification)
• ML Framework: scikit-learn
• Experiment Tracking: MLflow
• API: FastAPI
• Containerization: Docker
• CI/CD: GitHub Actions
• Monitoring: Custom logging with SQLite
• Validation: Pydantic

📁 Project Structure

├── data/                   # Dataset storage
├── src/
│   ├── data/              # Data processing modules
│   ├── models/            # Model training modules
│   ├── api/               # FastAPI application
│   └── monitoring/        # Logging and monitoring
├── notebooks/             # Jupyter notebooks for exploration
├── tests/                 # Unit tests
├── docker/                # Docker configuration
├── .github/workflows/     # GitHub Actions CI/CD
├── mlruns/               # MLflow tracking data
├── requirements.txt       # Python dependencies
├── Dockerfile            # Docker configuration
└── README.md             # This file

🚀 Quick Start

1. Setup Environment

   pip install -r requirements.txt

2. Train Models

   python src/models/train.py

3. Start API

   uvicorn src.api.main:app --reload

4. Build Docker Image

   docker build -t iris-mlops-pipeline .

📊 Model Performance

Model	Accuracy	Precision	Recall	F1-Score
Logistic Regression	93.33%	93.33%	93.33%	93.33%
Random Forest	90.00%	90.24%	90.00%	89.97%

Best model: Logistic Regression automatically selected based on performance

🔍 Monitoring

• Prediction requests are logged to SQLite database
• Metrics endpoint available at /metrics
• Request/response logging for audit trail

🧪 Testing

pytest tests/

📈 MLflow UI

mlflow ui

🐳 Docker Deployment

docker run -p 8000:8000 iris-mlops-pipeline

📝 API Documentation

Once running, visit: http://localhost:8000/docs

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