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FDE Architecture Framework: Build Production ML Systems That Don't Break

The most common reason ML systems fail in production isn't model quality — it's that prediction, business logic, and system action are tangled together in a single monolithic service. FDE separates them, and everything becomes easier.

Hi there,

The most common architecture mistake in production ML systems: putting the model, the business logic, and the system action in the same service. When the model changes, business logic breaks. When action APIs change, the model retrain. When business rules change, nobody knows what to redeploy.

FDE — Feature, Decision, Execution — separates these three concerns. Here's how to build it.

📚 New Tutorial

FDE Architecture Framework: Build Production ML Systems That Don't Break

  • What FDE is and why the monolith architecture fails at scale
  • The Feature layer: online feature serving, training-serving consistency, feature groups
  • The Decision layer: model serving, business rule engine, output contract
  • The Execution layer: action dispatching, idempotency, rollback, audit
  • Hands-on: implement the FDE pattern for a banking fraud decision system in Python
  • The interaction protocol between layers: request contracts, response contracts, error handling
  • Testing strategy: unit, integration, shadow mode, canary
  • Deployment patterns: independent layer scaling, layer-level canary releases

Read the full tutorial →

More posts dropping every day. Stay curious.

— Bhanu @ superml.dev