End‑to‑End Secure MLOps for Healthcare: From FHIR Ingestion to Model Serving on Kubernetes

You don’t need to be a clinician, but you should understand basic concepts like encounters, diagnoses, and labs. Pairing with clinicians or health informaticians is crucial; they help define sensible labels and spot clinically odd features.

Yes. The pipeline scales down as well as up. With small datasets you will rely more on simpler models, stronger regularisation, and careful validation.

Avoid real patient data unless you are inside a compliant environment with proper approvals. Use synthetic or de‑identified data for learning and prototyping, and apply encryption, access control, and logging in production.

For a single low‑traffic model, a managed API service or even one VM may be enough. Kubernetes and KServe shine when you need consistent deployment and security for many models or teams.