Watch this on-demand webinar to see how a feature store can accelerate machine learning by slashing the time spent on feature engineering by up to 80%.
In this webinar, we will review feature store requirements, describe the common patterns existing frameworks use and propose an HTAP database approach that simplifies and accelerates the feature store architecture.
The webinar is hosted by Ben Epstein, ML Lead, and Sergio Ferragut, Senior Solution Engineer, of Splice Machine.
Enterprise scale feature stores enable sharing and collaboration of features and eliminates duplicate work
It’s crucially important for data scientists to avoid repeating the work of their teammates. Spending hours building predictive features should only have to happen once, not every time an experiment needs to be tested. Simple, shareable feature stores are key to building highly productive data science teams. Create useful features, share them with your team, and keep them up to date. It’s as simple as that.
RDBMS ENABLES REAL-TIME FEATURE UPDATING
Keep your models trained on the most up-to-date set of features. Feature stores ensure stale data isn’t being used by tracking when features are updated and what they were updated to.
MODELS AS FEATURES
Deploy models directly to your feature store to add real-time intelligence to your feature sets. Gain unprecedented access to real-time machine learning.
EVENT-DRIVEN RFM AGGREGATION
Utilize database triggers to execute arbitrary SQL, Java or Python on an event-driven basis. Keep all of your real-time features up to date without human intervention.