Prediction APIs return forecasts, probabilities and confidence rather than records. That changes the engineering: inputs must be validated, outputs must be bounded and explainable, and the model has to be watched once it is live.
We build the data pipeline, the model and the serving layer as one system, because in practice they fail as one system.
What the work involves
- Historical data assembly and feature design
- Model selection and validation
- Forecast horizons and confidence intervals
- Backtesting and evaluation sets
- Monitoring for accuracy decay
Ready to scope it?
Tell us about the data, the callers and the answer you need the API to return.