# Module 8 Overview

## Theme

GPU workflows, scale, and deployment

## Essential Question

How do practitioners move from notebook experiments to reproducible GPU training pipelines?

## Professional Scenario

A prototype model is ready to leave a notebook, and the team needs a repeatable training job with accountable metrics and resource assumptions.

## Learning Outcomes

By the end of this module, students will be able to:

- explain the core technical idea in precise neural-network vocabulary
- connect architecture and training choices to the shape of the data and task
- run or interpret the module lab as reproducible evidence
- identify limitations, failure modes, and next steps for a defensible experiment

## Module Components

- Book prose for conceptual framing and practitioner patterns.
- Assignment notebook for the graded artifact: reproducible training workflow checklist.
- Slides and narration for structured teaching.
- Instructor notes for facilitation, misconceptions, and evidence expectations.
- Lab notebook for hands-on experimentation.
- Rubric for grading technical correctness, evidence, tradeoffs, risk analysis, and communication.