Please note: The English version of this success story was translated using AI to make it accessible to our international audience.
| Before | After |
|---|---|
| Unmaintainable Script Mess | Clean Abstraction Maintenance Becomes a Pleasure |
The client company operates a machine learning model. The model categorizes and tags bank transaction data.
To train this model, manually labeled training data is required. The assigned labels must adhere to certain business data quality rules. Previously, a script was used to check the labeled data for rule compliance.
The rules were hard-coded as low-level Pandas operations—without any abstraction.
Maintaining the data quality rules in the existing solution was causing significant problems:
A metamorphant partner developed a simple embedded DSL in Python in collaboration with the client. This provides the rule maintainer with meaningful building blocks. Rules are now described at a higher level of abstraction.
Each building block is testable in itself. Each rule is testable in itself.
The analysis revealed a key finding: recurring patterns emerged in the Boolean combination of multiple rules. The same combinations were frequently repeated in a copy-paste fashion. By capturing the underlying logic and extracting it into separate building blocks, these combinations were transformed into simple function calls: the complex chains were eliminated and are now rarely encountered in the daily work of maintainers.
The success of the project was quickly evident: after a prolonged period of stability, the model required further modification. The change was implemented in less than an hour, from start to finish—quality-assured and deployed, without costly context switches or waiting times, and without the risk of regressions in the existing rules.