Abstract: The world of Machine Learning and Artificial Intelligence is growing more rapidly than ever before. Creative AI ideas sprout at an unbeatable pace, organizations are exploring new emerging opportunities of Machine Learning and AI. While developments in Machine Learning and AI keep touching the edges of technology, the burning question remains: how to bring those ideas in action, and how to keep them alive?
In this session, Véronique Van Vlasselaer will talk about the often-forgotten steps after model development: What is required to turn Machine Learning and AI into value? What does it mean when one talks about deployment or operationalization of machine learning? How do we manage and govern machine learning models once they run in production? Véronique will show that Model Management & Governance should not be a burden for data scientists, but helps to ease the process to bring and keep Machine Learning and AI models alive.
Bio: Véronique is a Decision Scientist 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 under prof. dr. Bart Baesens. Her Ph.D is oriented towards the development of fraud detection frameworks and solutions from a data science perspective. She is co-author of the book “Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection”.