Cloud-Native MLOps Governance: A Practitioner Framework Linking Model Lifecycle Reliability to Enterprise Service Delivery Outcomes on Microsoft Azure

The operationalization of machine learning models in enterprise production environments-commonly termed MLOps-has matured rapidly as a practitioner discipline, yet a specific governance gap persists: the connection between model lifecycle reliability and the service delivery outcomes that enterprise delivery leaders are accountable for remains largely unaddressed in the literature and poorly instrumented in practice. Existing MLOps frameworks concentrate on deployment automation, drift detection, and compliance tooling, but rarely specify how model health signals should be translated into delivery-leadership decisions or accountability metrics. This paper introduces the Cloud-Native MLOps Governance Framework (CN-MGF), a seven-dimension governance model developed from direct engineering and delivery-leadership experience across enterprise deployments on Microsoft Azure. CN-MGF embeds a service delivery orchestration layer-termed the Delivery Intelligence Bus-at the architectural level, translating model health events into delivery-outcome language that non-ML-specialist delivery leaders can act on directly. The framework specifies a formal Model Health to Delivery Outcome Chain linking each governance dimension to a corresponding service delivery KPI, and a Delivery Leader Accountability Dashboard implemented on Azure Monitor, Azure Machine Learning, and Power BI. Practitioner observation across multiple enterprise engagements suggests that organizations adopting CN-MGF-aligned governance experience meaningfully fewer undetected drift incidents, faster remediation cycles, and improved AI-dependent SLA attainment relative to ad hoc governance baselines. These observations are presented as field-based practitioner findings rather than as the outcome of a controlled, multi-organization empirical study, and are offered to motivate further formal validation of the delivery-outcome linkage this paper proposes. The paper's principal contributions are threefold: a governance architecture treating service delivery orchestration as a first-class design concern; an explicit mapping from governance dimensions to delivery accountability metrics; and an Azure-native implementation blueprint enterprise architects and delivery leaders can adapt directly.