Features are the core of machine learning (ML) generated insights. Yet many organizations today are challenged with getting a wide spectrum of data types in the right format and just in time for both model training and model inference while also increasing collaboration across data science teams by making features discoverable and reusable.
Learn how Snowflake enables organizations to scale the speed at which AI/ML initiatives go from development to production and focuses on addressing key challenges in feature engineering.
Join our ML and Feature Store experts to see how to:
- Leverage Snowflake platform components to streamline access and processing of ML features
- Extend feature lifecycle management capabilities with open-source or commercial feature store solutions
- Learn about newly announced features that extend the power of Snowflake for real-time ML applications