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.