Design Patterns in Machine Learning Applications

Machine learning code still needs ordinary software design discipline. Training pipelines, model selection, and configurable workflows benefit from patterns that keep experiments, production code, and serving paths understandable.

Use this section to see where pipeline, strategy, and factory choices help Java ML systems stay modular instead of scattering workflow logic across notebooks, jobs, and services.

In this section

• Pipeline Pattern for Java ML Data Processing
  Structure Java machine learning workflows as explicit processing stages so data preparation, training, and evaluation stay composable and testable.
• Strategy Pattern for Algorithm Selection in Java ML
  Switch scoring or training algorithms in Java ML systems without branching through the whole workflow.
• Factory Pattern for Java ML Model Creation
  Use factories to select model families and configuration paths in Java ML systems without scattering construction logic across training and serving code.