Automated Feature Engineering for Enterprise Machine Learning

Abstract: 

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.

Bio: 

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.

Open Data Science

Open Data Science
Innovation Center
101 Main St
Cambridge, MA 02142
info@odsc.com

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