Production-ready machine learning pipeline with model versioning, caching, and monitoring.
This project establishes a robust foundation for deploying and managing machine learning models in production environments. The infrastructure features a FastAPI-based model serving layer with Redis caching for low-latency predictions. The system implements comprehensive model versioning, allowing seamless A/B testing and rollback capabilities. Real-time performance monitoring tracks prediction latency, model drift, and resource utilization. The entire stack is containerized with Docker and orchestrated through Kubernetes, enabling automatic scaling based on prediction load. This infrastructure now serves as the backbone for multiple ML applications across different projects.