Design Patterns in Machine Learning Code and Systems
Eugene Yan
The article discusses common design patterns from software engineering applied to machine learning code and systems, including factory, adapter, decorator, strategy, iterator, and pipeline patterns. It provides concrete examples from libraries like PyTorch, Gensim, Hugging Face, XGBoost, and Pandas showing how these patterns simplify data handling, model training, and pipeline construction. These patterns enable developers to write more maintainable, flexible, and reusable ML code by standardizing interfaces and allowing easy customization.
Why it matters
Understanding and spotting patterns to use code and components as intended.
