Machine Learning at Scale

Machine Learning at Scale explores the architecture, infrastructure, and operational practices required to build and manage enterprise-grade machine learning systems. The book covers real-time feature engineering, distributed training, model serving, MLOps, observability, vector databases, security, and cloud-scale deployment strategies, providing practical guidance for engineers designing reliable and scalable ML platforms.