ENGINEERING PROJECT
Scoring and forecasting for decision support
Three complementary approaches — explainable rules, predictive models and forecasting — supporting operational decisions.
Discuss your projectContext
Context
Risk decisions and operational forecasts do not all rely on the same data or require the same level of explainability. Some situations call for clear business rules; others justify training models on the available history. The work had to organise several approaches, paying particular attention to data quality and integration into existing processes.
Digital Place intervention
Digital Place intervention
Digital Place worked on preparing and analysing historical data, building risk indicators and developing predictive models for several use cases. The work also covered estimating operational durations and representing uncertainty. The team designed the services needed to use these results and organised processing for execution and monitoring. Each system followed its own decision logic rather than being reduced to a universal score.
Architecture
Architecture
Business data feeds preparation processes, models and reporting services. APIs expose the results to the relevant applications; orchestration and experiment tracking support the evolution of the approaches.
Engineering decisions
Engineering decisions
Match the method to the decision.
Distinguishing explainable rules, learned predictions and estimates with uncertainty avoids asking a single model to serve fundamentally different purposes.
Technologies
Technologies
- Docker
- FastAPI
- MLflow
- Pandas
- PostgreSQL
- Prefect
- Python
- XGBoost
- scikit-learn
Result & scope
Result & scope
- Business scoring
An assessment system based on interpretable indicators and adjustable parameters.
- Predictive risk analysis
Models and reporting designed to distinguish risk situations.
- Operational forecasting
Duration estimates that take uncertainty into account.
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