Machine Learning as Infrastructure: From Optional Capability to Foundational Digital Stack

This paper argues that machine learning has transformed from a specialized analytical tool into a foundational layer of modern digital infrastructure. It examines how ML systems are now deeply embedded across cloud platforms, enterprise systems, cybersecurity architectures, autonomous services, and digital governance mechanisms. The paper analyzes this transition through technological, organizational, economic, and governance perspectives, covering key developments such as cloud-native architectures, MLOps pipelines, foundation models, large language models, edge intelligence, and autonomous decision-making systems. The author presents comparative analyses between traditional software architectures and ML-centric infrastructures, discusses emerging operational paradigms including AI observability and AIOps, and examines future challenges involving sustainability, reliability, security, and regulatory compliance. The central finding is that machine learning should no longer be viewed merely as an intelligent application component but rather as an essential infrastructural layer supporting modern digital ecosystems.