Tag
model-evaluation
AI Foundations
Training, Validation, and Test Sets
Give fitting, model selection, and final evaluation separate data roles, then reproduce a small experiment without allowing test results to influence the chosen procedure.
AI Foundations
Case Study: From a Vague Request to a Verifiable AI Task
Turn a troubleshooting-assistant proposal into a bounded document recommendation task, with explicit evidence, a baseline, permissions, and testable acceptance criteria.
AI Foundations
Business Metrics and Model Metrics
Connect prediction quality to service outcomes, compare errors under explicit costs and capacity limits, and measure whether a model improves the work it supports.
AI Foundations
Defining Goals, Inputs, Outputs, and Constraints
Turn a delivery problem into a testable AI task: define the decision, available inputs, prediction target, operating limits, and evidence needed before release.