Do you have too many meaningless features? Semantic Feature Engineering may be the cure.


Feature engineering is vital to AI success, especially for tabular data. Yet feature engineering is little more than a footnote in most popular machine learning education courses.

One of the challenges in teaching and practicing ML feature engineering is the lack of a systematic approach based on understanding of data semantics and database structure. As a result, feature lists are often extremely bloated, containing unexplainable features which are difficult to maintain and make sense of.

Discover a new framework for feature engineering and a set of signal types that intuitively explain their purpose, align with underlying data semantics, are mathematically rigorous, and inspire more creative feature engineering.


Sergey is a data scientist with a background in physics and neurobiology. FeatureByte is Sergey's second startup. He was one of the first employees at DataRobot where he created and led a professional services group and helped the company grow into a unicorn. Sergey is widely known for being a Kaggle Grandmaster and holding the #1 rank on Kaggle in the past. Multiple times he was mentioned as one of the top data scientists by various publications. Sergey’s passion is in machine learning, predictive modeling and inventive feature engineering.

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