MLOps and the Industrialization of Machine Learning: A Cross-Disciplinary Framework for Scalable, Governed, and Ethically Accountable ML Systems

The operationalization of machine learning (ML) systems in
production environments has emerged as a defining challenge at the intersection of computer science, data science, and technology management. While substantial research has advanced ML model development, the systematic engineering of pipelines, governance structures, and organizational processes required to sustain ML systems at industrial scale remains comparatively underexplored. This chapter presents a cross-disciplinary synthesis of MLOps — Machine Learning Operations — as a scholarly and applied framework that unites software engineering rigor, operational
governance, and organizational design to close the persistent research-to-production gap. Drawing on foundational literature in systems engineering, ML deployment, and technology management, the chapter examines the full ML lifecycle, organizational maturity
models, toolchain architectures, ethical accountability mechanisms, and future research frontiers including LLMOps, AutoML governance, and edge ML deployment. The analysis advances the position that ML industrialization is inherently a sociotechnical phenomenon: its success depends as critically on deliberate organizational and governance design as on technical automation.