Tag
problem-framing
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.
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.
When You Don't Need Machine Learning
Recognize tasks that are better served by rules, queries, search, optimization, or human review, and compare the evidence and operating costs before adding a model.
Detection, Segmentation, Generation, and Decision-Making
Follow a parcel inspection from bounding boxes and pixel masks to a report draft and a permitted action, with worked overlap metrics and expected-loss calculations.
Classification, Regression, Ranking, and Clustering
Choose an AI task by its required output. Work through ticket routing, handling-time estimates, review priorities, and similarity groups with a verified NumPy example.
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.