Moving to predictive: How to assemble the beginnings of your feature store with Snowflake & dbt Labs

Historically, analytics has been focused on "what happened." And to this day, newer and newer generations of tooling, dbt for example, have come forth accelerating the speed and utility of data in an enterprise for decision making. Machine learning, on the other hand (the "what will happen"), has seemingly been stood up as a separate silo with an organization with seemingly "more intricate" technical requirements, the need for "data scientist", and done so all in the name of how to handle "more special" data resulting in "more accurate" decision making. In this session, you will learn how to cut through the noise and extend and leverage your analytic practice with Snowflake and dbt Labs into the realm of machine learning by pairing your analytical pipelines with a feature store layer to declaratively serve both model training and model scoring scenarios, even at some of the lowest latency (real-time) production requirements.