End-to-End ML System — Data Pipeline, Model, API & Monitoring

I will deliver a complete, production-ready ML system — from data pipeline through model training, experiment tracking, REST API deployment, and monitoring with data drift detection — covering every layer of the ML lifecycle in a coherent, maintainable architecture your team can operate and retrain as your data evolves. Production ML systems fail not because of model quality but because of infrastructure quality; data pipelines that don't handle schema changes, models deployed without monitoring, and retraining processes that require manual intervention combine to produce ML systems that degrade silently in production.

The system covers an automated data preprocessing pipeline, ML model with MLflow experiment tracking and registry, FastAPI inference endpoint with input validation, Docker containerisation, CI/CD pipeline for automated retraining and deployment triggers, Evidently AI data drift monitoring with alerting, and a runbook covering retraining, deployment, and incident response. A 7-day post-deployment support window is included.

This service is designed for organisations building their first production ML system or replacing an ad hoc model deployment with a properly engineered, monitored, and maintainable ML infrastructure.