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September 27, 2022

[Webinar] Machine Learning Use Cases: Data Science Solutions for Business Problems

About the Event

Excitement for data science and machine learning does not seem to stop in the current job market; companies still need a combination of flavours in analytics-oriented solutions. These solutions span from traditional dashboards and visualization as part of business intelligence to model deployment and monitoring as part of MLOps.

The technical complexity of solutions changes dramatically depending on the goal. One element, however, remains constant: each solution is expected to be designed to tackle a business problem and make easier the life of the user. This is not an easy task as requests to provide solutions start with open-ended and vague questions, and close to nontechnical details about it.

In this session, we present some real scenarios where data science and machine learning solutions have helped with replacing, enhancing, or creating processes to increase the productivity of business teams in organizations. The goal is to expose attendees to the struggles data science teams go through to create tech-based solutions. It is normal for these teams to constantly deal with the good, the bad, and the ugly.

Event Details
6:30 pm -
 8:30 pm EST/EDT
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Rogelio Cuevas
Director, Data Scientist (Sun Life) | Data Science Instructor (WeCloudData) | Career Mentor (WeCareer)
Rogelio Cuevas is an active member of the Toronto Data Science community who currently works as a Director of Data Science in the financial industry in Toronto and as a Data Science mentor for a San Francisco start-up. His educational background has allowed him to work in various research-oriented institutions that include Duke University, McMaster University, University of Western Ontario, and Fermilab. His past research activities made him a recipient of a postdoctoral Fellowship awarded by the Natural Science and Engineering Research Council of Canada. Rogelio holds a double bachelor’s degree in physics and mathematics, a master’s in physics and mathematics, and a Ph.D. from McMaster University during which he focused on theoretical and computational tools applied to quantum systems.
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