Version control eliminates the need for data preprocessing by automatically cleaning and preparing the data for model training.

Version control provides a systematic way to track and manage changes in code, configurations, and data, facilitating collaboration among team members and ensuring reproducibility of results.

Version control enhances the accuracy of machine learning models by automatically correcting errors in the code and data.

Version control guarantees the scalability of machine learning pipelines by optimizing the computational resources used during model training.

Version control allows for the comparison of different model versions and configurations, enabling the team to identify the most effective approaches and revert to previous versions if necessary.