Title: Deep Dive on Automating Feature Engineering
Date: Thursday, July 18, 2019
Time: 12:00 PM Eastern Daylight Time
Duration: 1 hour
IBM introduced AutoAI as part of Watson Studio. One of the technically challenging topics that can allude even the most experienced data scientists is feature engineering. In this inaugural monthly Webinar series, we will do a deeper dive on feature engineering.
Key takeaways:
- How IBM's AutoAI explores data to extract new features
- What techniques IBM's AutoAI uses under the hood for feature engineering
- Understanding why certain transformations make models more accurate
- Learn about the IBM Research Cognito Project
This special series is presented by the lead data scientists and domain experts across IBM. Register now to boost your data science skills to the next level.
Greg Filla
Senior Offering Manager, Watson Studio | Watson Machine Learning
IBM
Udayan Khurana
Staff Member, Research
IBM
Dr. Udayan Khurana is a research scientist at theIBM TJ WatsonResearch Center at Yorktown Heights. He conducts research in the field of Automated AI, ontopics includingfeature engineering, ensembles, autonomous agents, sampling amongst others. He has previously worked on large scale data indexing, query optimization, graph databases and temporal analytics. He graduated with a Ph.D. from the University of Maryland in 2015. He has published over 20 papers in top-rated conferences and published 5 patents.
Nicholas Mazzitelli
Full Stack Developer IBM Data & AI
IBM
As a member of the Watson Studio development team, Nick has contributed to a variety of features during his time at IBM including the design, build, and deployment of the Visual Recognition and Natural Language Classifier model builders. Most recently, his technical leadership and delivery of key features has led AutoAI experiments to a successful release.
Register at:
Event Registration
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Karen Wilkins
Client Technical Professional
IBM UK Ltd
Bristol
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