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Sessional Lecturer, INF2205H - Designing Sustainable & Resilient Machine Learning Systems with MLOps
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
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the likelihood of adoption and scale. Qualifications: A PhD or Masters level education with recent experience in clinical and health informatics, preferably in the areas of ICT adoption, implementation, and
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acceptance and adoption in the real-world. Students will use frameworks and techniques for architectural modeling, analysis, and design to understand explainability and fairness in the context of machine
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Estimated TA support: based on enrolment - None Qualifications: A PhD or Masters level education with experience in health informatics and information technology; A robust understanding of EHR
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: Weekly, 2 hours Estimated enrolment: 100 Estimated TA support: based on enrolment - None Qualifications: A PhD or Masters level education with experience in health informatics and information technology; A