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Abstract classes provide a blueprint for creating consistent, maintainable data cleaning pipelines in data science projects. By defining common methods like validate, save, and run in a base class while requiring project-specific implementations of load and transform methods, teams can ensure compatibility across different client datasets while reducing human error and improving code quality. The approach separates concerns between standardized output requirements and client-specific data processing logic, making pipelines more robust and easier to extend for new projects.

Abstract Classes: A Software Engineering Concept Data Scientists Must Know To Succeed

Example: Preparing data for ingestion into a feature generation pipeline

Final summary: Why use abstract classes in data science pipelines?