Feature versioning is a crucial practice in machine learning and software development that involves managing different versions of features used in models. As models evolve, new features may be added, existing ones modified, or obsolete features removed. This process ensures that the model remains robust and adaptable to changing data and requirements. By implementing versioning, teams can track changes, maintain consistency, and facilitate collaboration across various stages of development. It also aids in debugging and allows for easy rollback to previous versions if needed, ultimately enhancing the reliability and performance of machine learning solutions.