Engineers and analyzes the trade-offs in large-scale AI systems. This course connects model architecture design with the practical, distributed systems required to train and serve them. Covers the full engineering lifecycle, including data curation, distributed training, inference optimization, and scaling large language models.

Recommended prerequisite: strong programming proficiency in Python, experience with a modern ML framework (e.g., PyTorch), and proficiency with git and command-line environments.

Course Logistics

Lectures: Tuesdays & Thursdays, 2:00–3:15PM
Location: MBE 155 (Miramontes Baca Education Bldg)
Office hours: posted as an announcement in Slack

This website is the source of truth for the schedule and readings.

  • Announcements and discussion: Slack
  • Assignments and starter code: the course GitHub organization
  • Assignment submission: Gradescope

See the logistics page for course structure, grading, and policies.

Instructors

Christoffer Heckman
Office: ECES 130
ARPG Research Group

Mark Zhao
Office: ECCR 1B26, Engineering Center
Office Hours: Mondays, 1:15 - 2:15 PM, ECCR 1B26
BASIL Research Group

TAs / Course Staff

Nolan Brady
Email: Nolan.Brady@colorado.edu
Office Hours:
 Tuesday/Thursday: 10:00 AM - 11:00 AM
 Zoom Link: https://cuboulder.zoom.us/j/2980052323