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A Dataiku Technical White Paper
A Primer on Data Drift
& Drift Detection Techniques
Monitoring model performance drift is a crucial step in production machine learning (ML); however, in practice, it proves challenging for many reasons. In this white paper:
- 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.
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from the Experts at Dataiku
About the Authors
Simona Maggio & Du Phan
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.