Module 6 Narration

Module 6 Narration#

Opening#

Open with the professional setting: an analytics team choosing a predictive model for an operational decision. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.

Middle#

Move through the module in four passes:

  1. Define Time, drift, and monitoring in the context of Machine Learning & Predictive Modeling.

  2. Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.

  3. Compare a baseline with an AI-enabled or more sophisticated alternative.

  4. Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.

Closing#

Close by returning to the module artifact: predictive modeling report with baseline comparison, validation evidence, and model card focused on time, drift, and monitoring: Define drift checks and monitoring metrics for a deployed predictor.. Students should leave knowing exactly what artifact they are producing and how it will be judged.