SYDE 670Deployment of Deep Learning Models
- Units
- 0.50
- Level
- Graduate
- Faculty
- Engineering
- Subject
- Systems Design Engineering
SYDE 670 as listed in the Fall 2026 Graduate Calendar. The main page has the current version, when it runs, and seats.
This course focuses on the deployment of computer vision networks for real-world applications. Students will gain practical knowledge on various computer vision tasks such as semantic segmentation, object detection, optical flow, and depth estimation. The course covers all critical aspects from selecting appropriate network architectures, data preparation and handling, to deployment on edge devices. The course also delves into infrastructure setup, training, and model compression techniques to ensure efficient deployment. By the end of the course, practitioners will be equipped with the skills needed to devise, implement, and maintain robust computer vision solutions in diverse real-world scenarios.
Prerequisites
None listed.
Antirequisites
- Not completed nor concurrently enrolled in:
Leads to
No course lists it as a requisite.
Check it elsewhere for Fall 2026
- Graduate Calendar, Fall 2026The official entry
- Requirements for Fall 2026Waterloo’s requisite checker
- Schedule of Classes, Fall 2026Instructors, rooms, live seats
- Quest Class SearchWhere you enrol
- Course outlinesSyllabi from past terms (UW login)
- UW FlowStudent ratings and reviews
- WClassesPast sections back to 2004, and who taught them
Calendar versions
Graduate Calendar
- Spring 2026
- Fall 2026 (shown here)
- Winter 2024 and earlier in the calendar archives