Dataset Versioning Without the Tools: A Practical Approach for Reproducible Machine Learning

Introduction Reproducibility is a cornerstone of rigorous machine learning practice. Yet in production ML systems, reproducibility often breaks at the data layer. A model trained on dataset-v1.2.3 performs differently from one trained on dataset-v1.2.4, but engineers struggle to articulate why. The conventional wisdom is to adopt specialised data versioning tools: DVC (Data Version Control), MLflow, or Weights & Biases. These tools are powerful. They’re also often unnecessary when you’re starting out...