5 Critical Mistakes When Building Modular AI Architecture (And How to Avoid Them)

What Goes Wrong When Theory Meets Production Modular architecture sounds perfect in principle: independent components, clean interfaces, easy scaling. Then you implement it and discover that your inference latency has tripled, your data scientists spend time debugging service communication than improving models, and you have seven different versions of the same feature calculation scattered across modules. These aren't edge cases - they're predictable consequences of common design mistakes that derail even experienced teams.