Member-only story
5 Software Architecture Patterns Every Senior ML Engineer Should Know
Machine learning systems rarely fail because someone forgot how gradient descent works.
They fail because training code becomes tightly coupled to infrastructure, pipelines turn into untestable monoliths, model artifacts lose their provenance, or deployment logic grows directly into training code.
At some point, an ML project stops being a collection of notebooks and becomes a software system.
And that changes the engineering problem.
A production ML system has databases, queues, object storage, APIs, workers, model registries, feature pipelines, monitoring, and deployment workflows. The model itself is only one component.
Here are eight software architecture patterns that help keep those systems maintainable.
1. Put Models Behind Ports and Adapters
One of the easiest ways to create technical debt in an ML system is to let framework-specific APIs spread throughout the application.
Imagine code that directly depends on a scikit-learn model:
prediction = sklearn_model.predict(features)Soon this call appears in API handlers, batch jobs, evaluation code, tests, and monitoring.
