Automated Feature Engineering for Enterprise Machine Learning


Good machine learning algorithms don’t guarantee good models. Great models need great features. That’s why feature engineering takes so much time. Developing good features is time-consuming, difficult, and most of all requires domain knowledge. Join dotData CEO, Ryohei Fujimaki, PhD, as he discusses the future of Feature Engineering, how automation can help you extract the full-potential of your data, and how you can leverage AI-features to augment and reinforce your AI/ML workflow.


Lulu is currently a Senior Data Science Solutions Architect at dotData, where he helps clients from various industries build end-to-end ML pipelines and deploy them into production. Prior to dotData, he was a DataOps Engineer at Tamr helping clients solve data quality and integration issues at large scale. Lulu has a Ph.D. in Physics from the University of Michigan.

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