# Module 4 Overview

## Theme

Distributed processing and scale

## Essential Question

When does scale require distributed computation?

## Module Components

- `Book prose`: conceptual framing, domain scenario, methods, and failure modes
- `Assignment`: evidence-backed production of a specific artifact
- `Slides`: presentation sequence for seminar or lecture delivery
- `Narration`: spoken version of the slide flow
- `Instructor notes`: facilitation plan, discussion prompts, and grading cues
- `Rubric`: criteria for evaluating the module artifact
- `Notebook`: executable lab aligned with the module theme using synthetic pipeline events with freshness, schema drift, lineage completeness, volume, and access-risk indicators

## Module Artifact

AI data platform design review with lineage, quality checks, cost controls, and access model focused on distributed processing and scale: Profile batch processing and identify bottlenecks.

## Professional Setting

Students work as if advising a platform team designing data infrastructure for repeatable model training and monitoring. Their work must be intelligible to data engineer, ML engineer, security architect, data steward, and platform owner.