Large machine learning pipelines can suffer from repetitive edits when stage definitions expand or change.
Note: This article references the academic demonstration version of the pipeline.
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Large machine learning pipelines can suffer from repetitive edits when stage definitions expand or change. Jinja2 templates streamline this process by centralizing all configuration details into a single, parameterized YAML structure. Each transformation stage is defined by placeholders-such as commands, dependencies, and outputs-so adding or modifying a stage involves updating a Hydra config rather than copying and pasting YAML blocks.
This approach follows several best practices:
The result is a more maintainable, error-resistant system that reduces manual overhead and ensures consistent formatting across all pipeline stages. This pipeline design also allows for validation of rendered configurations, catching syntax or logic errors early. As a result, building and iterating on complex pipelines remains both transparent and scalable.
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