Jim Dowling Presents:
Python-centric Feature Stores
Most enterprise data used by Data Scientists to train machine learning models is tabular data that comes from data warehouses and data lakes. Recent growth in the popularity of the modern data stack, based on lakehouses like Snowflake, Delta Lake, Big Query, and Redshift, have led to growth in the use of SQL-centric tools for data engineers, such as DBT. However, Data Scientists' language of choice is Python. How do we square this circle?
In this talk, Jim Dowling investigates the role of the Feature Store for machine learning in enabling Python native access to enterprise data for both training and serving features to models. In particular, Dowling describes the problem of how to create point-in-time consistent training data from features spread over many tables using a SQL backend from Python.
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