MLOps in the Enterprise: Practitioner Framework for AI Model Lifecycle Governance in Large-Scale Azure Cloud Deployments

Abstract

The operationalization of machine learning (ML) models in enterprise production environments - commonly termed MLOps - has emerged as one of the most consequential yet academically undercharacterized challenges in applied artificial intelligence. While existing literature rigorously addresses model architecture and training methodology, the operational concerns determining whether deployed models deliver sustained business value remain comparatively unexplored: model drift detection, retraining governance, compliance checkpoint integration, rollback protocol design, and the observability infrastructure required to distinguish genuine model degradation from environmental signal shifts. Critically absent from the MLOps literature is a formal treatment of how AI model lifecycle reliability connects to service delivery key performance indicators (KPIs) - the SLAs, escalation rates, client satisfaction scores, and delivery throughput metrics that senior delivery leaders are held accountable for. This paper presents a conceptual synthesis of MLOps governance patterns drawn from practitioner experience across large-scale enterprise AI deployments on Microsoft Azure cloud infrastructure, spanning three ML workload categories. We introduce the Cloud-Native MLOps Governance Framework (CN-MGF), a structured seven-dimension governance model that addresses the full model lifecycle while embedding service delivery orchestration at the architectural layer and providing delivery leaders with explicit accountability metrics linking model health to measurable business outcomes. Illustrative patterns drawn from practitioner engagements suggest that CN-MGF-aligned organizations can achieve substantially lower rates of undetected model drift incidents, faster mean time to remediation, fewer compliance audit findings, and improved AI-dependent service delivery SLA attainment. These observed patterns point toward a plausible chain from MLOps governance quality to service delivery performance, repositioning MLOps governance as a strategic service delivery investment rather than a purely technical risk management activity.