Machine learning projects often involve frequent changes to code, datasets, model configurations, and environments. Reproducing a specific result, whether for debugging, collaboration, or deployment, can become a significant challenge. Simple code version control using Git helps, but doesn't address issues like tracking large datasets or logging the exact parameters used for a particular model training run.

This chapter lays the groundwork for understanding why managing these elements systematically is necessary. We will examine the common difficulties faced when trying to reproduce machine learning experiments. You will learn why standard version control tools alone are not adequate for the unique demands of ML workflows, particularly concerning data. We'll clarify what reproducibility means in this context and identify the essential components that need to be tracked. Lastly, we introduce the basic ideas behind data versioning and experiment tracking, concepts we will build upon throughout the course using tools like DVC and MLflow.