MLOps / 2026
Kubernetes ML Platform
A static-friendly MLOps architecture for reproducible training, model services, and observability.
Problem
Research models often move through notebooks, scripts, and one-off environments before anyone asks whether they can run reliably. That gap slows iteration and makes reproducibility fragile.
Approach
The platform uses containerized training jobs, declarative infrastructure, model artifact tracking, and simple service boundaries. The design keeps research loops flexible while making production constraints visible early.
Architecture
Diagram placeholder: Git, CI, container registry, Kubernetes jobs, artifact store, model registry, inference service, and monitoring.
Results
The architecture reduces environment drift, clarifies ownership between research and platform layers, and supports repeatable experiments without requiring heavyweight backend complexity.
Learnings
The best platform work feels quiet: it removes friction, documents assumptions, and gives researchers fast feedback without hiding operational reality.