Sessional Lecturer, INF2205H - Designing Sustainable and Resilient Machine Learning Systems

University of Toronto • Toronto, ON

1d • Onsite

About The Position

This course, INF2205H, examines state-of-the-art techniques and technologies related to MLOps (Machine Learning Operations). MLOps refers to the application of continuous delivery and continuous integration (CI/CD) principles and practices for designing sustainable and resilient machine learning systems. MLOps helps designers of machine learning systems to address the challenge associated with the ever-changing nature of data that is conveyed by the adage “model drifts as data shifts”. Students will use frameworks and techniques for architectural modeling, analysis, and design to understand and apply theoretical and practical aspects of MLOps.

Requirements

• Completed, or nearly completed, PhD degree in an area related to the course or a Master’s degree plus extensive professional experience in an area related to the course.
• Located in geographical proximity to the applicable University premises in order to attend and perform duties on University premises as of the Starting Date.

Nice To Haves

• Teaching experience is preferred.

Responsibilities

• Preparing course materials
• Delivering course content (e.g., seminars, lectures, and labs)
• Developing and administering course assignments, tests & exams
• Grading
• Holding regular office hours

What This Job Offers

• Job Type: Part-time
• Career Level: Mid Level