Build an evidence-led process for detecting input and prediction shifts, investigating causes, and evaluating a controlled response.
Connect statistical drift signals with production-model diagnosis and governed improvement. Distinguish input-distribution change from change in predictive relationships, combine data-quality and outcome evidence, and plan a response that does not equate an alert with a reason to retrain. Fictional assignments emphasize baselines, time-aware evaluation, subgroup review, rollback, and ownership.
Differentiate covariate shift, concept drift, data defects, and ordinary variation in monitored predictions. Design monitoring measures with reference windows, label delays, subgroup checks, and explicit limitations. Propose a controlled model-change workflow with time-aware evaluation, approval, and rollback criteria.
Familiarity with supervised-learning evaluation, basic statistics, and production data workflows is recommended. Coding is optional; assignments use fictional cases. Avoid personal data, treat signals as evidence for investigation rather than proof of harm, and do not automate consequential changes without accountable review.
Learners with basic familiarity who are ready to build practical, independent skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Intermediate · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.