Engineering a Flexible Model Interface

 This week, my focus has been on software architecture, specifically how the pipeline will interact with the various DeepLense models. The DeepLense project includes a diverse set of models—classifiers, regressors, and generative models—each with slightly different input and output requirements. To avoid writing custom code for each one, I've designed a ModelInterface using an abstract base class.

The goal is to create a consistent API that allows the pipeline to load and run any compatible model with a single, unified command. This upfront investment in a flexible architecture should make the system much easier to maintain and extend in the future as new models are developed. It’s a classic software engineering problem, and solving it correctly now will prevent significant headaches down the road.