Practice English vocabulary for feature stores in ML: feature pipelines, online vs. offline stores, feature serving, point-in-time correctness, and feature reuse.
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A centralized system stores pre-computed features that both training and serving can use. What is this system called?
A 'feature store' is a centralized platform that stores, manages, and serves features — the engineered inputs to ML models — for both offline training and online inference.
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During real-time inference, the model needs to look up the latest feature values for a user in milliseconds. Which part of the feature store handles this?
The 'online store' is the low-latency storage layer of a feature store, typically backed by Redis or similar, used for real-time feature lookups during inference.
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During model training, historical feature values spanning months of data are retrieved in batch. Which part of the feature store handles this?
The 'offline store' is the batch storage layer of a feature store, typically backed by a data warehouse or object storage, used for historical feature retrieval during model training.
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When training a model, you must ensure that features used for each training example only include data that was available at the time of that example — not future data. What is this property called?
'Point-in-time correctness' (also called point-in-time joins) ensures that training data only uses feature values that were available at the prediction time of each training example, preventing data leakage.
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Instead of each team recomputing the same features independently, teams share pre-computed features from a common store. Which phrase describes this benefit?
'Feature reuse' is one of the main benefits of a feature store — teams can discover and use features already computed by other teams, reducing duplication and ensuring consistency.