A text classifier that maps a natural language business problem description to the most likely data engineering solution category.
💡 How to state a business issue
Write 1-2 sentences the model can classify reliably:
- Describe the data situation - what systems and data you have, and why the need is hard to meet. Don't just express frustration ("I have no idea what's happening").
- State the desired outcome - the concrete result or constraint you want ("one consolidated monthly report", "alerts within milliseconds").
- Describe the need, not the solution - don't name or negate a category; "we don't have a data warehouse" leaks the answer and confuses the model. Let it infer the label.
- Keep it to one issue - one problem per description, not several bundled together.
❌ Weak: "I have no idea what's happening with our finances. I don't see profits per category. We don't have a data warehouse."
✅ Strong: "Our finance data is spread across several systems and we can't get profit broken down by product category. We need one consolidated place with monthly profit-by-category figures for management reporting."
1from src.inference import Predictor
2predictor = Predictor()
3result = predictor.predict("We need to detect fraud before transactions are approved.")
4print(result["predicted_label"])
5
The model favoured label "data_governance" in these cases:
I have no idea, what is happening with our finances. I don't see anywhere profits per category. We don't have data warehouse.
