juniorMLOps
What is feature engineering in MLOps?
Updated May 17, 2026
Short answer
Feature engineering transforms raw data into meaningful inputs for ML models.
Deep explanation
It includes scaling, encoding, and aggregations. In MLOps, feature consistency between training and serving is critical.
Real-world example
Converting timestamps into weekday and hour features for demand prediction.
Common mistakes
- Creating training-only features not available in production.
Follow-up questions
- What is a feature store?
- Why is consistency important?