Machine Learning (ML) has become essential for precise decision-making in businesses, necessitating robust model versioning tools to manage the iterative nature of ML processes. Model versioning involves tracking changes in ML models, including code, data, and artifacts, to facilitate reproducibility and maintain a clear development cycle, critical for collaboration and accountability. The article introduces six model versioning tools: Neptune, ModelDB, DVC, MLflow, Pachyderm, and Polyaxon, each offering unique features for tracking experiments, managing data and models, and ensuring efficient ML workflow. These tools help in maintaining a model's lineage, comparing performance metrics, and organizing implementation code, which are crucial for AI applications in fields like autonomous vehicles, healthcare, and stock trading. The importance of model versioning is underscored by its role in preventing issues such as losing valuable code or pushing underdeveloped models to production, emphasizing the need for choosing the right tool that aligns with specific project requirements and budget considerations.