The dvc.yaml file plays a central role in orchestrating a DVC-based pipeline. By consolidating raw data ingestion, transformations, feature engineering, and modeling into a single file, it serves as the primary source of truth.
Note: This article references the academic demonstration version of the pipeline.
Some implementation details have been simplified or removed for IP protection.
Full implementation available under commercial license.
The dvc.yaml file plays a central role in orchestrating a DVC-based pipeline. By consolidating raw data ingestion, transformations, feature engineering, and modeling into a single file, it serves as the primary source of truth. This approach aligns with recognized best practices: it reduces version control conflicts, simplifies contributor onboarding, and creates a clear, linear stage flow.
Atomic transformations form another key advantage. Instead of scripts dedicated to individual pipeline steps, each script is designed to be data-version-agnostic and standardized in both input and output. This standardization ensures that each stage references only one file, making maintenance and auditing more straightforward.
Challenges such as repetitive configuration or the management of large pipelines can be addressed by programmatically generating dvc.yaml via Jinja2 templates. This allows users to enumerate transformations in code, minimize errors, and automate updates.
Several benefits arise from this structure, including reduced compute costs—only modified stages rerun—and robust versioning, since Git tracks every change. Ultimately, a well-organized dvc.yaml file becomes the backbone of reproducible and maintainable machine learning pipelines.