Architecting a platform to enable deployments and maintenance of machine learning models is not as straightforward as conventional software architectures. The most common misconception among software developers and data scientists is that a machine learning project lifecycle consists of just training a single successful model and deploying it in a service. The real world scenario is much more complicated than this. In fact, assuming that there would always be a single model in service for a p...
