#data-science

Feature Engineering Best Practices for Structured Data in the Age of Foundation Models

Feature engineering for structured data in 2026 is a hybrid discipline. Traditional techniques — encoding, scaling, interactions, target encoding — remain the foundation, while LLM augmentation adds semantic enrichment capabilities that especially shine on text-heavy tabular data.

AutoML Platforms in 2026: Which Tools Actually Reduced Time-to-Model for Data Teams

Feature Engineering in the LLM Era: What Changed, What Didn't, and What Actually Matters

Feature engineering has been a cornerstone of machine learning for decades. Data scientists spend weeks transforming raw data into structured inputs that models can exploit. Then large language models