Join this Tech Talk to learn about how these solutions come together to provide an easy-to-use mechanism to reduce time-consuming model preparation tasks, create universal feature definitions, and bridge the gap between AI and BI teams.
Listen to presentations and best practices from top AI technologists on how data scientists can now create, select, and store business-vetted features for machine learning and modeling re-use. Learn how to leverage the compute power of Snowflake, the flexibility of Feast, and the power of AtScale’s semantic layer to automate and accelerate time to predictive insights.
You’ll learn how a semantic layer and a feature store can address why a majority of data science projects fail and how this integration ultimately serves to ensure model prediction outputs impact your business in a meaningful way.
You will learn:- How Snowflake and AtScale are working together to help data scientists create production-ready ML models from the context provided in a semantic layer- Major pain points within the data science workflow that prevent machine learning models from being put into production environments- The importance of using a feature store to streamline data science operations, and why so many data science teams are turning to Feast