On-Demand Webinar

Understanding Complex Machine Learning Models

Discover how you can get more from your modeling efforts to drive results.

About the webinar

It’s never been easier to build machine learning models. So why are so many machine learning models not effectively used?

One of the reasons is that complex machine learning models often lack the transparency and trust that an organization needs to confidently rely on their decisions.

Understanding black-box machine learning models and assessing whether their logic is in line with business expectations is important in a modern AI-driven organization.

Watch this webinar to hear business and academic experts share how organizations can realize trust in machine learning models to better reap the benefits.

Why attend?

  • Learn key considerations for trustworthy machine learning.
  • Hear developments in academic research that drive trust in models.
  • Discover how data-centric AI closes the loop.
     

Duration: 60 minutes

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About the Experts


Véronique Van Vlasselaer

Sr. Data Scientist, Analytics and AI, SAS

Véronique Van Vlasselaer is the Analytics and AI lead for South, West and East Europe at SAS, and a true data science enthusiast. In her job, she passionately helps companies to envision and prepare for an AI-driven future, embrace the power of data science to support intelligent decisioning, and discover the real value in their data. Before she joined SAS, she graduated as Doctor in Business Economics at the KU Leuven (Belgium) with the department of Information Management and Decision Sciences. She is co-author of Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection.


Marinela Profi

Data Scientist and AI Product Marketing Manager, SAS

Over the past 5 years, Marinela Profi honed her skills developing and deploying at scale AI and analytics solutions for manufacturing, retail and banking industries, using SAS and open-source tools. She is based in Raleigh, North Carolina, United States, where she enjoys speaking at tech conferences and mentoring people who want to start a career in data science.


David Martens

Professor of Data Science at the University of Antwerp, Belgium  

David Martens is Professor of Data Science at the University of Antwerp, Belgium. He teaches data mining and data science and ethics to postgraduate students studying business economics and business engineering. David’s work with Foster Provost on instance-based explanations is regarded as one of the first to have introduced counterfactual explanations to the AI domain. His research has been published in high-impact journals and has received several awards. David is also the author of the book “Data Science Ethics: Concepts, Techniques and Cautionary Tales”, published by Oxford University Press.

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