Smart MLOps: Supporting Machine Learning Pipelines with Drift Detection and Model Adaptation

This project aims to enhance responsible AI in dynamic environments by leveraging data drift detection and model adaptation techniques. By collaborating with IFS Canada, this project seeks to synergize academic exploration with real-world application, creating a dynamic partnership that bridges the gap between theoretical advancements and practical solutions. This project, focused on developing reliable AI solutions, seamlessly aligns with IFS’s core values of innovation and optimizing business processes. Key among IFS’ principles is ‘Trust’. In this collaboration, we commit to reinforcing this value by prioritizing AI model reliability and transparency. Through rigorous validation and accountability, our research aims to create AI models that consistently deliver accurate insights. This mirrors IFS Canada’s ‘Trust’ value, reinforcing their reputation as a dependable provider of enterprise software solutions.

Faculty Supervisor:

Olga Baysal

Student:

Partner:

IFS Canada

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Carleton University

Program: