MLOps Demand Forecasting

Comprehensive MLOps pipeline for retail demand forecasting with MLflow, XGBoost, and Kubernetes

🎯 Project Overview

This project demonstrates enterprise-grade data engineering and MLOps practices with production-ready implementation.

🛠️ Tech Stack

Core Technologies:

• Python 3.9+
• Apache Spark / PySpark
• Apache Kafka
• Apache Airflow
• Docker & Kubernetes
• PostgreSQL / MongoDB / Redis

Cloud & Infrastructure:

• AWS (S3, EMR, Redshift, Lambda, EKS)
• Terraform for IaC
• CI/CD with GitHub Actions

📋 Prerequisites

# Install Python dependencies
pip install -r requirements.txt

# Install Docker (if not already installed)
# Follow: https://docs.docker.com/get-docker/

# Install Terraform (if not already installed)
# Follow: https://learn.hashicorp.com/tutorials/terraform/install-cli

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/Amanroy666/MLOps-Demand-Forecasting.git
cd MLOps-Demand-Forecasting

2. Set Up Environment

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Copy environment template
cp .env.example .env
# Edit .env with your configurations

3. Run with Docker

# Build and start services
docker-compose up -d

# Check logs
docker-compose logs -f

# Stop services
docker-compose down

📁 Project Structure

MLOps-Demand-Forecasting/
│
├── src/                    # Source code
│   ├── data/              # Data processing modules
│   ├── models/            # ML models (if applicable)
│   ├── utils/             # Utility functions
│   └── config/            # Configuration files
│
├── notebooks/             # Jupyter notebooks for exploration
├── tests/                 # Unit and integration tests
├── docker/               # Docker configurations
├── terraform/            # Infrastructure as Code
├── airflow/              # Airflow DAGs and configs
├── docs/                 # Additional documentation
│
├── docker-compose.yml    # Docker compose configuration
├── requirements.txt      # Python dependencies
├── .env.example         # Environment variables template
└── README.md            # This file

🔧 Configuration

Key configuration files:

• config/config.yaml - Application configuration
• .env - Environment variables (create from .env.example)
• docker-compose.yml - Docker services configuration

📊 Features

• ✅ Scalable data processing with Apache Spark
• ✅ Real-time streaming with Apache Kafka
• ✅ Workflow orchestration with Apache Airflow
• ✅ Containerized deployment with Docker/Kubernetes
• ✅ Infrastructure as Code with Terraform
• ✅ Comprehensive monitoring and logging
• ✅ CI/CD pipeline with automated testing

🧪 Testing

# Run unit tests
pytest tests/unit/

# Run integration tests
pytest tests/integration/

# Run with coverage
pytest --cov=src tests/

📈 Performance Metrics

• Throughput: [Metric details]
• Latency: [Latency details]
• Uptime: [Availability details]
• Cost Optimization: [Cost savings details]

🔐 Security

• All sensitive data encrypted at rest and in transit
• IAM role-based access control
• Secrets management with AWS Secrets Manager
• Network isolation with VPC and security groups

📝 Documentation

Detailed documentation available in the docs/ directory:

• Architecture Design
• API Documentation
• Deployment Guide
• Troubleshooting

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Aman Roy (Amez)

• LinkedIn: @amanxroy
• GitHub: @Amanroy666
• Email: contactaman000@gmail.com

🙏 Acknowledgments

• Built with modern data engineering best practices
• Follows industry-standard MLOps workflows
• Implements enterprise-grade security and scalability patterns

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