Why are we training ML models wrong and how can feature stores help? - Episode 101

In this week's episode, we talk about the problem of data leakage, which occurs when data scientists feed data that did not exist during the time of a past event to machine learning models. 

Monte Zweben, CEO of Splice Machine talks about how feature stores can help with this issue by validating when a data set actually occurred and then correcting these point-in-time consistency issues.