Looking for advice on revamping a disorganized Python package used for ML model development and prediction services, and suggestions on starting with solid software architecture practices.
hey silvia, have u thought about using container-based designs to isolate components? sometimes a small shift can clear up a lot of messy code. what part of the ml-service interface is givin u the most issues?
hey silvia, i really like ur post! have u thought about how to balance modularity with testing? restructuring might open cool new aproachs for ml models. what challenges have u faced so far in reorginizing the package?
hey silvia, u might wanna decouple ml logic from service code. keeping them separate eases testing and maintainability. what specific hurdles are u facing?
Based on my experience, restructuring a Python project can yield significant improvements in clarity and maintainability. I found that establishing a clear separation between machine learning logic and service orchestration is a key step. Introducing a well-defined package structure early on not only simplifies testing but also eases scalability as the project grows. It is worthwhile to invest time creating concise documentation during the reorganization process, as it helps new contributors understand the codebase faster and reduces the risk of introducing bugs.