Alexis Bondu
Alexis Bondu is a Machine Learning researcher at Orange Research. His fields of research are varied and cover machine learning (Auto ML), active learning, weakly supervised learning, time series, data streams and early decision making. He is also responsible for the research part of the Khiops project, which is an Auto ML solution developed over the last twenty years in-house at Orange, and which has now been distributed as Open Source for around two years. The aim of this research work is to prepare the new functionalities and algorithms that will appear in future versions of Khiops.
Session
While most machine learning tutorials and challenges focus on single-table datasets, real-world enterprise data is often distributed across multiple tables, such as customer logs, transaction records, or manufacturing logs. In this talk, we address the often-overlooked challenge of building predictive features directly from raw, multi-table data. You will learn how to automate feature engineering using a scalable, supervised, and overfit-resistant approach, grounded in information theory and available as a Python open-source library. The talk is aimed at data scientists and ML engineers working with structured data; basic machine learning knowledge is sufficient to follow.