End-to-End ML System — Data, Training, Serving & Monitoring

I will build a complete end-to-end ML system — from automated data preprocessing through model training with experiment tracking, production API serving, drift monitoring, and a retraining pipeline — covering every layer of the ML lifecycle in a coherent, maintainable architecture. Organisations that build each ML layer independently without a unified architecture accumulate technical debt at every interface — data pipelines that don't match training expectations, serving infrastructure that can't handle model updates, and monitoring that isn't connected to retraining — all problems that a coherent end-to-end system design eliminates.

The system covers an automated preprocessing pipeline, MLflow experiment tracking, a training pipeline with hyperparameter optimisation, a REST API serving infrastructure with Docker, Evidently AI monitoring for drift detection, an automated retraining trigger, and a system architecture document for ongoing maintenance.