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.

This session requires pre-registration. Sign up here. If session is filled you are welcome to come to the room and join the waitlist onsite. Open seats will be made available 10 minutes after session start.

Check the slides here: https://docs.google.com/presentation/d/1H-aPsc2DkPGcJUV4pd_iLSdMMMAMZTEtsAFO4hpglXM/edit#slide=id.g15a4510fa6a_0_560

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.