Application and Practice of Machine Learning Infrastructure Optimization in Advertising Systems

Abstract

The explosive growth of advertising data has increasingly challenged the performance and predictive accuracy of traditional advertising platforms. Optimizing and upgrading machine learning infrastructure has emerged as a critical solution to address these challenges. This optimization encompasses the enhancement of computing hardware, the development of scalable and efficient distributed processing architectures, and the refinement of model training, tuning, and deployment strategies. Such improvements enable advertising platforms to handle massive volumes of data more efficiently while delivering more precise insights. This article explores the practical applications of machine learning infrastructure optimization in advertising systems, including personalized ad targeting, accurate estimation of advertising effectiveness, proactive fraud detection and risk management, and intelligent allocation of advertising resources. Empirical evidence suggests that these optimizations not only improve the efficiency and quality of ad placement but also play a pivotal role in maintaining system stability, reducing operational costs, and supporting more informed, data-driven decision-making in dynamic advertising environments.