Automating DVC Pipelines with Templates

This article explains how templates are structured, how Python scripts generate the final dvc.yaml, and why this method has significantly improved the maintainability of a large MLOps pipeline.

Tags: mlops advanced-templates DVC automation DRY

Category: MLOps: Designing a Modular Pipeline

Automating with Templates

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.

Introduction

Manually editing dvc.yaml whenever a transformation is added or a parameter changes can be both tedious and error-prone. To address this challenge, a templating system was introduced. This article explains how templates are structured, how Python scripts generate the final dvc.yaml, and why this method has significantly improved the maintainability of a large MLOps pipeline.

The Problem with Manual YAML
Each stage in dvc.yaml typically includes a cmd (the only required field), deps, and outs section. A pipeline with 20 transformations might require 20 nearly identical stage definitions. If a parameter needs updating or a script is renamed, each stage’s definition must be edited manually-an error-prone process, particularly under tight deadlines or when collaborating with multiple contributors. In a larger pipeline with dozens of transformations, the overhead grows exponentially.
The Solution
A template file is set up to resemble a typical dvc.yaml, but placeholders are inserted for all parameters. A Python script, retrieves a list of transformations, then populates the template for each stage.

This approach keeps dvc.yaml in sync with the Hydra configurations at all times, so new transformations can be added by adjusting the config rather than editing YAML directly. This eliminates repetitive tasks and ensures that each pipeline stage follows a standardized format.

Handling Updates & Diffs
Overwriting manually edited dvc.yaml files is a concern. To address this, the Prefect generation process includes a diff check. If differences exist, the script prompts the user or logs a warning, ensuring that unintended changes do not go unnoticed. In practice, manual edits are rare because the template captures most scenarios, and unusual requirements are better handled within the Hydra configs themselves.

Conclusion

templating has proven invaluable in keeping dvc.yaml concise and up to date. Even as the pipeline expands, the overhead of adding or modifying stages remains small. Templating enforces consistency, encourages best practices, and supports a DRY (Don’t Repeat Yourself) philosophy. Further details about the file and Python script are available in the repository. Future posts will examine how the pipeline logs each step’s output and metadata, integrating seamlessly with tools like Hydra, DVC, and MLflow.