Our current C# and React system in a Kubernetes cluster must integrate Python-based ML models. How can architecture best separate data science work while addressing production concerns?
In my view, establishing a clear separation between the production components and the machine learning pipeline is critical. Decoupling Python-based modules from the C# and React ecosystem not only simplifies integration but also empowers data scientists to iterate quickly without risking production stability. Containerizing the ML models as independent services and defining strict API contracts can lead to a smoother and safer deployment process. This approach reduces interdependencies, allowing data science teams to experiment and scale their models in isolation while still contributing effectively to the overall application.