Engineering
Research code should be reproducible, testable, and safe to extend.
The engineering layer is there to make the modelling trustworthy. The aim is not flashy infrastructure; it is work that can be rerun, checked, extended, and explained without guesswork.
Repository Structure
- Separate data preparation, modelling, evaluation, and reporting so each step is easy to inspect.
- Keep project-specific logic explicit rather than hiding it inside one large script.
Reproducibility
- Use config-driven runs with visible assumptions.
- Keep raw inputs immutable and save the outputs that support the case study.
Testing
- Test utility functions and transformations that could quietly break the pipeline.
- Check for schema drift, missing fields, and impossible values before modelling.
- Run sanity checks on model outputs so probabilities and summaries stay coherent.
Practical Use
- Each project includes the extra work that would still be needed before it could be used more widely.
- That usually means better data coverage, more monitoring, clearer analyst guidance, and tighter handling of edge cases.