Designing Sustainable and Resilient Machine Learning Systems with MLOps


Decision-makers in modern organizations rely on machine learning systems to infer insights from information by analyzing meaningful patterns in the connections and associations within data. Leaders in many sectors of our economy and society utilize these systems to support evidence-informed decision-making using techniques such as regression, classification, clustering, and collaborative filtering. 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 everchanging 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.