Design Patterns are reusable, formalized constructs that serve as templates to address common problems in designing efficient systems. These enable the development of high-performance, resilient & robust applications.

Widely-used design patterns, especially from the object-oriented paradigm, include:
▪︎ Behavioral Patterns: Command, Mediator, Memento, Observer, Visitor, etc.
▪︎ Creational Patterns: Builder, Factory, Prototype, Singleton, etc.
▪︎ Structural Patterns: Adapter, Bridge, Composite, Decorator, Façade, Flyweight, Proxy, etc.

Newer ones have evolved in recent years, such as those focused on Microservices & Event-Driven architectures. Apart from the software ones, Machine Learning applications also need data-centric and modeling-centric design patterns, such as Checkpointing, Ensembling, Feature-Crossing, Hashed Features, Rebalancing & Transfer Learning.

AI/ML architects should also deploy lesser-used and emerging design patterns, such as 2-Phase Prediction, Active Learning, Bridged Schema, Cascading, Fairness Lens, Feature Stores, Stateless-Serving, Useful Overfitting & Windowed-Inferencing. Care should also be taken to prevent anti-patterns like Abstraction & Dependency Debt, Common Smells, Entanglement, Pipeline Jungles, etc.