# Module 3 Narration

## Opening

Open with the professional setting: a platform team designing data infrastructure for repeatable model training and monitoring. 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 **Storage, indexing, and retrieval** in the context of Big Data Management for AI Applications.
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: **AI data platform design review with lineage, quality checks, cost controls, and access model focused on storage, indexing, and retrieval: Compare tabular, document, and vector storage options.**. Students should leave knowing exactly what artifact they are producing and how it will be judged.