Blueprints for Scaling Machine Learning Systems in Ad Technology

Abstract

Machine learning systems in advertising technology demand robust architectural foundations to handle high-throughput requirements while maintaining reliability and cost efficiency. The implementation of feature stores serves as a critical infrastructure component, supporting both real-time inference and batch training workflows through distributed caching and storage optimization. Data quality and governance frameworks ensure system reliability through automated validation pipelines and comprehensive monitoring. MLOps pipelines facilitate sustainable operations through automated training infrastructure, deployment strategies, and observability mechanisms. Performance optimization techniques enhance system efficiency through feature serving improvements and model optimization. Cost management strategies incorporate resource optimization and operational efficiency measures. Retraining mechanisms maintain model freshness through automated triggers and efficient pipeline design. Comprehensive experimentation frameworks accelerate innovation while maintaining statistical rigor, enabling rapid iteration and validation of new approaches. Privacy-preserving techniques balance effective personalization with regulatory compliance and ethical considerations, incorporating federated learning, differential privacy, and robust consent management. The combination of these elements creates scalable, reliable machine learning systems capable of meeting the demanding requirements of modern advertising technology while maintaining operational efficiency, cost-effectiveness, and ethical integrity.