Clinical MLOps: A Framework for Responsible Deployment and Observability of AI Systems in Cloud-Native Healthcare Platforms
TL;DR
Clinical AI systems operate under constraints that extend beyond typical production settings, and technical robustness alone is insufficient; governance and accountability become integral system properties.
Abstract
Machine Learning Operations (MLOps) practices have reached a notable level of maturity within general-purpose software engineering. Pipelines are standardized, monitoring is automated and deployment patterns are well rehearsed. Yet when these same practices are transferred into clinical environments, their adequacy becomes less certain. The healthcare domain introduces operational and ethical pressures that conventional MLOps frameworks were not originally designed to address. Clinical AI systems operate under constraints that extend beyond typical production settings. Stringent data protection regimes including GDPR and the EU AI Act shape how data can be processed and retained. Model drift is not merely a statistical inconvenience; it may alter clinical decisions with tangible consequences for patient outcomes. Regulatory compliance demands comprehensive, tamper-evident audit trails. At the same time, decision-critical contexts impose a clear expectation of human oversight. In other words, technical robustness alone is insufficient; governance and accountability become integral system properties.