Learn/Course 02
Foundations · weeks 2–3

Problem framing, statistics & evaluation

An ETA model improves RMSE while increasing missed-delivery cost.

Opening production brief

A measurable problem, before a model.

Users

Operators, product owners, and people affected by the evaluation decision.

Data

decision utility, prediction cutoff, censoring, temporal splits with version, owner, event time, and availability contracts.

Constraints

An ETA model improves RMSE while increasing missed-delivery cost. Every solution must state latency, cost, capacity, and fallback limits.

Success

Offline evidence plus a deployable resolution: A metric contract, leakage-safe validation, calibration analysis, and experiment plan.

CHAPTER 1

Frame the decision

Turn the product request into a decision and label contract.

  1. 01

    Predict decisions, not just labels

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

    65–90 min · checkpoint · deterministic lab
  2. 02

    Labels, availability, and leakage

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

    65–90 min · checkpoint · deterministic lab
CHAPTER 2

Validate honestly

Build leakage-safe baselines and validation.

  1. 03

    Baselines and honest splits

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

    65–90 min · checkpoint · deterministic lab
  2. 04

    Metrics, thresholds, and calibration

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

    65–90 min · checkpoint · deterministic lab
CHAPTER 3

Choose metrics, objectives, and policy

Select metrics, optimization losses, thresholds, uncertainty, and online evidence from the task.

  1. 05

    Uncertainty and online experiments

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

    65–90 min · checkpoint · deterministic lab
  2. 06

    Metric selection by task and decision

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

    65–90 min · checkpoint · deterministic lab
  3. HW

    Late-delivery evaluation contract

    Specify a cutoff-safe target, validation design, cost/capacity threshold, calibration report, uncertainty, and controlled experiment.

    Rubric · staged hints · reference resolution
Prerequisite guidance

1 concepts make this faster.

Prerequisites are guidance, never hard gates.

C01Engineering foundationsA seeded, tested, packaged, versioned training workflow with a model card.
Ready to work the problem?

Predict decisions, not just labels

Start lesson 01