Process standardization for machine learning systems: A reference model based on international standards

Process standards have long provided a foundation for software engineering, but equivalent guidance for machine learning projects remains limited. Existing frameworks, such as ISO/IEC 5338, outline artificial intelligence lifecycle processes at a high level but lack the detail needed to guide engineering teams in practice. This paper proposes a Process Reference Model for machine learning and artificial intelligence systems that extends existing standards by providing detailed descriptions of the technical processes that define their lifecycle. The process model was developed through a structured methodology aligned with ISO/IEC 33004 and ISO/IEC 24774 and designed following the principles of Design Science Research. It focuses exclusively on technical processes, specifying their purposes, outcomes, inputs, outputs, and base practices in a consistent format. The resulting process reference model provides a standard-aligned foundation for defining, comparing, and assessing machine learning development processes, and supports future efforts in lifecycle standardization and process capability assessment.