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

Abstract

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. The framework addresses key challenges, including training-serving skew, system resilience, and computational efficiency through structured logging, automated validation mechanisms, and graceful degradation strategies. The article proposes a multi-tiered monitoring approach that tracks model predictions, feature distributions, and system health indicators, enabling proactive issue detection and resolution. The article details architectural considerations for implementing canary deployments, shadow deployment capabilities, and circuit breaker patterns, ensuring system reliability under varying production conditions. Additionally, the article discusses strategies for optimizing resource utilization through feature value caching and intelligent invalidation mechanisms. This article contributes to the growing knowledge on production ML system design by providing a structured approach to building and maintaining robust, scalable, and reliable machine learning systems in production environments.