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To train fairly, each label needs the feature values that were known before the event, not after. Mixing up time leaks the future into your model. ⏳

Label leakage happens when a feature secretly carries information from after the prediction moment. The model looks brilliant offline, then flops live.

Joining today's account balance onto a loan made last year is a classic leak. The right value is the balance as it was at loan time.

A point-in-time join matches each event to the latest feature value that existed at or before that event's timestamp. No peeking ahead.

You hand Feast an entity dataframe: entity keys plus an event timestamp for each training row you want features for.

Call get_historical_features with that dataframe, and Feast does the time-correct join against the offline store for you.

Call to_df on the result to get a training dataframe where every row carries correct, leak-free feature values.

If the nearest value is older than the feature's ttl, Feast leaves it null instead of attaching something out of date.

Historical reads hit the offline store for training; online reads hit the online store for serving. The feature definition stays identical, so there is no skew.

Point-in-time correctness is what makes offline scores you can trust. Skip it and your validation numbers are a comforting lie. 🎯

Define once, materialize for speed, and join by time for training. Together these give you a feature store that is fast, consistent, and honest.

Why use a point-in-time join when building a training set?

You used point-in-time joins via get_historical_features to build leak-free training sets, while the same definitions serve online. That is a complete feature store loop. 🏁