Detect Changes & Anomalies in Data

After obtaining a PhD in Biomedical Image Processing in 2011, Simona Maggio worked in several companies (CEA, Thales, Rakuten) as a Research Engineer in Computer Vision and Natural Language Processing for applications ranging from video surveillance to document digitization and e-commerce. She's now Senior Research Scientist at Dataiku, exploring MLOps topics, such as model debugging, robustness and interpretability.

Du Phan is a Machine Learning engineer at Dataiku, where he works in democratizing data science. In the past few years, he has been dealing with a variety of data problems, from geospatial analysis to deep learning. His work now focuses on different facets and challenges of MLOps.

• Take a brief but deep dive into the underlying definitions and logic behind drift

• Explore a real-life use case presenting different scenarios that can cause models and data to drift

• Delve into two techniques (the domain classifier and the black-box shift detector) used to assess whether dataset drift is occurring