MACHINE LEARNING MODELS IN PRODUCTION: A SYSTEMATIC FRAMEWORK FOR SCALABLE AND ROBUST DEPLOYMENT

Abstract

This article presents a comprehensive framework for deploying and productizing machine learning models in real-world industrial settings, addressing the critical gap between laboratory development and production implementation. Through a systematic analysis of 47 enterprise-scale ML deployments across diverse industries, we identify key challenges and establish best practices for transforming experimental models into robust production systems. The methodology encompasses four primary dimensions: technical integration architecture, operational excellence, continuous monitoring systems, and feedback loop implementation. The article reveals that successful ML productization requires more than model accuracy alone; it demands a holistic approach incorporating automated retraining pipelines, sophisticated monitoring systems, and scalable infrastructure. Results indicate that organizations implementing our proposed framework achieved a 64% reduction in deployment failures, 41% improvement in model maintenance efficiency, and 73% faster time-to-production compared to traditional deployment approaches. Furthermore, we introduce a novel scoring system for assessing production readiness of ML models, validated across multiple use cases. The article contributes to both theoretical understanding and practical implementation of ML systems at scale, offering concrete guidelines for practitioners while identifying areas for future research in automated ML operations and systematic deployment strategies.