MACHINE LEARNING IN ADTECH: TRANSFORMING DIGITAL ADVERTISING

Abstract

This comprehensive article explores the transformative impact of machine learning technologies on digital advertising, focusing on the evolution and implementation of advanced ML systems across the AdTech pipeline. It examines the intricate processes involved in modern advertising technology, from creative content analysis to real-time bidding optimization. The article details how machine learning algorithms revolutionize audience targeting, bid optimization, and campaign performance through sophisticated data processing and decision-making systems. It discusses key technical implementation considerations, including model architecture design and system infrastructure requirements for large-scale ML deployments in advertising platforms. Additionally, the article addresses emerging challenges and future directions in the field, particularly concerning privacy regulations, model improvements, and system optimizations. Special attention is given to privacy-preserving techniques, including federated learning and cookie-less tracking alternatives, as well as advancements in self-supervised learning and edge computing. This article provides insights into how machine learning continues to shape the future of digital advertising while balancing innovation with privacy and regulatory compliance.