It is creating new features from existing features that may help in improving the model performance. An essential purpose of feature engineering in production ML is to reduce computing resources, and this is done by concentrating predictive information in fewer features to promote computing efficiency. Inconsistencies in feature engineering can introduce training-serve skews, leading to poor serving model performance. These

inconsistencies arise due to:

• Training and serving code paths are different (e.g., train in Python but serve in Java), resulting in different transformations between the two
• Diverse deployment scenarios (e.g., model deployed in different environments like mobile, web, and server)

Tasks:

• Feature Selection
• Feature Creation
• Feature Transformation

Benefits:

• Improved accuracy of the model
• Improved generalization of the model