AI engineering glossary · Feature engineering data prep
What is training-serving skew?
Training-serving skew: A mismatch between the features a model saw in training and those it receives in production, caused by different code paths, data sources or timing. It silently degrades performance and is prevented by sharing the same feature logic in both places.
Training-serving skew: an example
Training computed 'average spend' with Python, while production uses an SQL query that treats nulls differently, so values differ for the same customer.
Related terms
- Feature store
- Pipeline
- Data drift
- Data validation
More Feature engineering data prep terms
- Undersampling
- Unit vector scaling
- Unknown category handling
- Variance threshold
- Weight of evidence
- Window feature
- Winsorization
- Wrapper method
Where you learn it
Training-serving skew is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.