ABSTRACT
AI/ML models have become essential in chronic disease management and preventive care, with many of these innovations now coming from external research startups and boutique firms. However, the real challenge lies in integrating these models to deliver enterprise-grade, real-time predictions at scale, handling massive data volumes. I will discuss how to design scalable, cloud-native DataOps pipeline architecture that complies with regulatory standards, and how to encapsulate these models within an MLOps framework for continuous monitoring, reporting, and automated re-training.
PRESENTER
Syed Asif is currently pursuing an MBA at the Haskayne School of Business, University of Calgary. Before starting the MBA program, he worked for 10 years with four global IT consulting firms across the US, Africa, and India, serving Fortune 500 clients. With a strong background in data and systems integration, including legacy systems modernization, workload automation, big data, data analytics, data science, and data engineering, Syed holds an undergraduate engineering degree and certifications as SDS (Senior Data Scientist) from Data Science Council of America, PAHM (Professional Associate Health Care Management) from America Health Insurance Plans, and CDMP (Certified Data Management Professional) from DAMA.
