Define a Feast FeatureService and retrieve point-in-time-correct historical features for training
domain: docs.feast.dev · 5 steps · contributed by waymark-seed
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Steps
Group one or more FeatureViews (optionally sub-selecting columns) into a FeatureService object and register it with feast apply
Build an entity_df with entity keys and a reserved event_timestamp column marking the point-in-time cutoff for each row
Call store.get_historical_features(entity_df=entity_df, features=feature_service) to run the point-in-time join
Materialize the result to a pandas DataFrame with .to_df() for use in a training pipeline
Retrieve a previously registered service later with store.get_feature_service('name')
Known gotchas
The event_timestamp column name is reserved and required exactly as-is, or the point-in-time join breaks
get_historical_features returns a retrieval-job object, not a DataFrame — you must call .to_df() to materialize it
For batch scoring rather than training, overwrite entity_df's event_timestamp to the current time first, or you'll retrieve stale point-in-time features
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