concept:uncertainty

Uncertainty

Uncertainty in the context of production machine-learning systems.

Lessons

Formal explanations, examples, simulations, and checkpoints.

Use predict decisions, not just labels to move the evaluation production brief toward a defensible release.

Use labels, availability, and leakage to move the evaluation production brief toward a defensible release.

Use baselines and honest splits to move the evaluation production brief toward a defensible release.

Use metrics, thresholds, and calibration to move the evaluation production brief toward a defensible release.

Use uncertainty and online experiments to move the evaluation production brief toward a defensible release.

Select metrics from the prediction target, decision, error costs, prevalence, slices, horizon, and deployment constraints—not from habit.

Use forecast contracts and naive models to move the forecasting production brief toward a defensible release.

Use trend, seasonality, and known-future signals to move the forecasting production brief toward a defensible release.

Use backtesting without time travel to move the forecasting production brief toward a defensible release.

Use statistical and ml forecasters to move the forecasting production brief toward a defensible release.

Use intervals, drift, and forecast operations to move the forecasting production brief toward a defensible release.

Dependency-authoritative learning units.

A metric contract, leakage-safe validation, calibration analysis, and experiment plan.

Assignment: Late-delivery evaluation contract Course 07

Rolling validation, statistical baselines, boosted forecasts, and uncertainty bands.

Assignment: Regional demand forecast

Reported facts and course reconstructions.