CAPTURE: A Stakeholder-Centered Iterative MLOps Lifecycle
Source abstract
Abstract
Current ML lifecycle frameworks provide limited support for continuous stakeholder alignment and infrastructure evolution, particularly in sensor-based AI systems. We present CAPTURE, a seven-phase framework (Consult, Articulate, Protocol, Terraform, Utilize, Reify, Evolve) that integrates stakeholder-centered requirements engineering with MLOps practices to address these gaps. The framework was synthesized from four established standards (ISO/IEC 22989, ISO 9241-210, CRISP-ML(Q), SE4ML) and validated through a longitudinal five-year case study of a psychomotor skill learning system alongside semi-structured interviews with ten domain experts. The evaluation demonstrates that CAPTURE supports governance of iterative development and strategic evolution through explicit decision gates. Expert assessments confirm the necessity of the intermediate stakeholder-alignment layer and substantiate the participatory modeling approach. By connecting technical MLOps with human-centered design, CAPTURE reduces the risk that sensor-based AI systems become ungoverned, non-compliant, or misaligned with user needs over time.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Ethics and Social Impacts of AI · Human-Automation Interaction and Safety · Persona Design and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Information Systems and Databases, RWTH Aachen University, Ahornstr. 55, 52074 Aachen, Germany
Fraunhofer FIT, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
Funding
BAFA
References (100+)
Show source-provided references
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