What is the process of importing feature values computed by your feature engineering jobs into a feature store called?

What is the process of importing feature values computed by your feature engineering jobs into a feature store called?

• A. Feature publishing
• B. Feature ingestion
• C. Feature extraction
• D. Feature submission

Answer: (B)

The process of importing feature values computed by your feature engineering jobs into a feature store is referred to as feature ingestion. This term emphasizes the act of bringing or transferring data into the feature store, where it can be stored, managed, and retrieved efficiently for future machine learning model training and serving. Feature ingestion is a critical workflow in machine learning pipelines because it ensures that high-quality, well-engineered features are readily available for model training and prediction. This process typically involves validating the features, possibly transforming them, and ensuring that they align with the requirements of the features already present in the store. In contrast, other terms like feature publishing might imply making features available to end-users, while feature extraction refers to the process of deriving new features from raw data rather than the importing of existing ones. Feature submission also does not accurately capture the action of transferring feature values into a feature store as it suggests a different connotation related to submitting models or results for evaluation or deployment. Thus, feature ingestion best encapsulates this critical phase in the machine learning workflow.

The process of importing feature values computed by your feature engineering jobs into a feature store is referred to as feature ingestion. This term emphasizes the act of bringing or transferring data into the feature store, where it can be stored, managed, and retrieved efficiently for future machine learning model training and serving.

Feature ingestion is a critical workflow in machine learning pipelines because it ensures that high-quality, well-engineered features are readily available for model training and prediction. This process typically involves validating the features, possibly transforming them, and ensuring that they align with the requirements of the features already present in the store.

In contrast, other terms like feature publishing might imply making features available to end-users, while feature extraction refers to the process of deriving new features from raw data rather than the importing of existing ones. Feature submission also does not accurately capture the action of transferring feature values into a feature store as it suggests a different connotation related to submitting models or results for evaluation or deployment. Thus, feature ingestion best encapsulates this critical phase in the machine learning workflow.