MLOps with GitHub Actions

Our MLOps implementation with GitHub Actions demonstrates how to create a robust, automated machine learning pipeline that ensures model quality and deployment reliability. This solution streamlines the entire ML lifecycle from development to production.

The challenge was to create a seamless workflow that could handle model training, testing, and deployment while maintaining version control and reproducibility. Our solution implements a comprehensive CI/CD pipeline specifically designed for machine learning projects.

Key Features: - Automated model training and evaluation - Version control for models and datasets - Automated testing of model performance - Seamless deployment to production - Monitoring and alerting integration - Reproducible environments

Technical Stack: - GitHub Actions for CI/CD - Python for ML development - Docker for containerization - MLflow for experiment tracking - Prometheus for monitoring - Kubernetes for orchestration