3 ms·
I am not sure how this answer my question. I understand that a feature store contain the features that have signal. My question is about the operational aspect
by streetcat1 6y ago
I am not sure how this answer my question. I understand that a feature store contain the features that have signal.
My question is about the operational aspect of online vs offline stores.
I.e. what happen if the features needed for a prediction are not in the ONLINE store?
- skingkong 6y agoYeah in this type of architecture there is some sort of join required. Interestingly, @maycotte talking about the Molecula feature store – it serves real-time and historical features in the same storage layer. Significant performance gains with that approach.
- jamesblonde 6y agoIf you want to reuse features, and not write a new pipeline for every model, you need JOINs. Both for the online and offline feature stores.
- jamesblonde 6y agoYou can do real-time feature engineering in your application as well. But we recommend to do it in a feature pipeline that also syncs the features to the feature store. We have a talk about it coming out at the spark/ai summit: https://databricks.com/session_eu20/real-time-feature-engineering-with-apache-spark-streaming-and-hof https://databricks.com/session_eu20/real-time-feature-engine...