34Build an ML training set in SQL with point-in-time-correct feature joins (no future leakage).▼hardUberDoorDashDatabricks2 replies◆ premiumThe single most common way SQL leaks the future into a training set is a careless join to a feature table. Point-in-time correctness is the fix, and it is an as-of join. Here is how to write it.Open full answer →
02What is a feature store, and how does it prevent training-serving skew?▼mediumDatabricksMicrosoftAmazon2 repliesunlockedA feature store is easy to define and easy to get wrong. The signal is explaining the exact bug it prevents (training-serving skew) and the point-in-time correctness that stops label leakage. Here is the full picture.Open full answer →
