SCALING MACHINE LEARNING PIPELINES: A COMPREHENSIVE GUIDE TO LARGE-SCALE PERSONALIZATION SYSTEMS

Abstract

Scalable machine learning pipelines are fundamental to modern personalization systems, yet implementing them efficiently at scale remains challenging. This article presents novel architectural patterns for high-volume personalization systems, introducing a unified approach that achieves sub-100 ms latency at the 99th percentile while processing billions of daily interactions. Through analysis of distributed storage systems, feature engineering, and model serving layers, we demonstrate that careful optimization of pipeline components can reduce resource utilization by 40% while maintaining recommendation accuracy. The key innovations are, hybrid architecture combining real-time feature computation with intelligent cache management and enables consistent performance even during peak loads. Using an e-commerce recommendation system as a case study, this article validates that this approach scales linearly up to millions of concurrent users while requiring minimal infrastructure overhead. The findings provide a practical blueprint for building robust personalization systems that can adapt to growing data volumes while maintaining strict performance guarantees.