Machine Learning and AI Architecture: A Comprehensive Framework for Production-Grade Intelligent Systems

This research proposes a modular, scalable end-to-end machine learning architecture to solve the persistent gap between experimental model development and production deployment. Traditional implementations often omit systematic data ingestion, feature engineering, monitoring, and automated retraining, leading to model degradation and high maintenance costs. The proposed six-layer framework—data ingestion, processing and storage, model development and training, deployment and serving, monitoring and drift detection, and security and governance—integrates technologies such as Apache Kafka, feature stores, MLOps pipelines, and automated drift detection for quality assurance. Experimental results show a ~60% reduction in deployment time, 92% accuracy in real-time drift detection, and automated retraining that keeps model performance within defined thresholds. Supporting both cloud-native and hybrid environments, this reference architecture helps practitioners translate machine learning theory into robust, production-grade systems.