Managing the complexity of machine learning workflows is a significant challenge, as these projects often involve not just code but also large datasets, model maintenance, and extensive experimentation. While traditional version control tools like Git are effective for software development, they do not fully accommodate the unique requirements of ML workflows, such as tracking multiple dataset versions, managing evolving models, and maintaining experiment histories. Specific utilities and frameworks have been developed to address these challenges, and this paper explores some of these available tools in detail. Incorporating structured workflows and best practices for managing artifacts helps ML practitioners improve reproducibility, scalability, and collaboration across teams. Furthermore, these tools can be leveraged as part of an end-to-end ML pipeline combined with CI/CD practices to facilitate tasks such as data preprocessing, model training, and deployment solutions. Through a hands-on case study of a retail recommendation system, this paper demonstrates how these techniques effectively tackle real-world challenges, including handling dynamic datasets, optimizing iterative experimentation, and maintaining model integrity. Finally, the paper explores emerging trends such as automation and sustainability in ML workflows, highlighting how integrating these strategies can enhance scalability and enable teams to build more efficient and production-ready ML systems.

Version Control Machine Learning Engineering Data Version Control (DVC) Experiment Tracking Model Reproducibility