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.