One of the fundamental ideas for creating scalable and maintainable machine learning (ML) systems is modularization. Modularity separates a system into smaller, independent components, making it easier to scale, debug, update, and manage. Modular design promotes improved group cooperation, component reuse, and adaptability to new features or changes. In this chapter, we will discuss the main ideas of modular design, how to apply modular structures in ML systems, and their advantages in scaling and supporting intricate solutions. If you are working on a real-world project, modularity is essential; without it, integration and maintenance challenges will grow substantially over time. The complexity of integrating new features, updating components, and reusing code across projects can be dealt with by means of modular design principles. Creating ML systems that can evolve without compromising performance, maintainability, or clarity depends on these fundamental ideas.