Version control is a critical aspect of machine learning and data science, facilitating efficient collaboration, traceability, and management of changes across projects. It encompasses various systems, including local, centralized, and distributed version control systems, each with distinct methods for storing and tracking file changes. Moreover, version control plays a crucial role in both software engineering and data science, though the latter involves a more exploratory approach necessitating detailed tracking of datasets, models, and experiments to ensure reproducibility and replicability. The article underscores the importance of data provenance and outlines strategies for data versioning, emphasizing the need for robust tracking to manage the complexity of artifacts such as datasets, models, and pipeline code. It also explores the challenges and strategies related to machine learning pipelines, model versioning, and experiment tracking, highlighting the necessity of systematic versioning to ensure reliable and transparent machine learning operations. The significance of reproducibility and replicability is underscored, emphasizing their role in validating research findings and maintaining trust in machine learning systems.