Feature engineering involves systematically transforming raw data into meaningful and informative features (predictors). It is an indispensable process in machine learning and data science. This ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and ...

Learn More. I spoke with Razi Raziuddin, CEO of FeatureByte, about the best way to prep data for ML models; he also explained some of the most common challenges with feature engineering.

Feature engineering is the process of selecting, transforming or creating relevant input variables or attributes called features from raw data to improve the performance of a machine learning model.

Despite the AI hype, ML tools really are proving valuable for leading-edge chip manufacturing. More aggressive feature scaling and increasingly complex transistor structures are driving a steady ...

Learn More Today, DataStax announced that it is acquiring privately-held AI vendor Kaskada, which develops a feature engineering platform that can help organizations use data for AI applications.