Reproducibility in machine learning ensures that an experiment can be repeated by others to achieve the same results. Version control systems, such as Git, are fundamental to this practice. They track changes to code, configurations, and can be integrated with tools to version data and models. By capturing the exact state of all assets used in an experiment, version control provides a reliable foundation for recreating the training environment and process, thereby ensuring reproducibility.

A. Data labeling is a data preparation step. While the version of the labeled data is important for reproducibility, the act of labeling itself does not ensure it.

C. Manual testing is a method for quality assurance; it is often inconsistent and not a systematic approach for ensuring an entire ML experiment can be reproduced.

D. Visualization helps in understanding results but does not provide the mechanism to repeat the process that generated those results.

1. Microsoft Documentation, "Git integration for Azure Machine Learning". In the "Why use Git integration?" section, it states, "Reproducibility: With the commit ID/tag, you can identify the version of the code that was used to run a specific job."

2. Microsoft Documentation, "What is MLOps?". Under the "Reproducibility" section, it emphasizes, "Implementing MLOps means you'll version your code, data, and models." This directly links versioning to reproducibility.

3. Chiticariu, L., et al. (2013). A-C-E: A Data-centric Language for Text Extraction. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (SIGMOD '13). The paper discusses the importance of tracking provenance and versions for reproducibility in data-centric systems. (This is a general principle applicable here).