Designing fully local machine learning systems: modular architecture and schema driven UI generation
I have been working on the design of a desktop system for running machine learning and generative models fully locally, and I am interested in feedback on a few architectural decisions.
The system is designed around three main principles:
All execution happens locally on the user’s machine, with no reliance on external APIs or cloud services.
The architecture is modular, allowing new models and algorithms to be integrated as independent components without modifying the core system.
User interfaces are automatically generated from structured schemas (for example Pydantic models), instead of being manually implemented for each model or workflow.
I am trying to understand whether these ideas are practically useful in real machine learning workflows or whether they introduce unnecessary constraints.
Some questions I would be interested in discussing:
Where do you see the biggest limitations of fully local ML systems today?
Does modular plugin based design actually scale in practice for ML tooling?
Is schema driven UI generation useful beyond simple prototypes or internal tools?
Would appreciate any technical perspectives or experience with similar systems.