ENGINEERING ROBUST MACHINE LEARNING SYSTEMS: A FRAMEWORK FOR PRODUCTION-GRADE DEPLOYMENTS

Production machine learning systems require a comprehensive engineering approach beyond model accuracy metrics.This article presents a systematic framework for developing and maintaining robust ML systems in production environments.The article introduces a holistic methodology encompassing critical aspects of production ML systems: comprehensive monitoring infrastructure, feature engineering pipeline design, deployment strategies, and resource management protocols.