Deploying and evolving machine learning (ML) solutions presents unique challenges in healthcare due to stringent regulatory requirements. This paper discusses the requirements for an extended MLOps framework that supports the certification of ML models as medical devices. By incorporating automated compliance checks, documentation generation, and continuous monitoring, we aim to facilitate adherence to standards and guidelines. This approach could enable healthcare ML models to maintain compliance throughout their lifecycle, fostering a smoother transition from prototype to clinical deployment.