We are thrilled to showcase the Classification Model Builder as a demonstration, highlighting only a fraction of the new AI capabilities Snowflake has introduced over the past year. Utilizing Streamlit as a frontend enhances the demo's coherence, offering viewers insights into potential implementations within their own data environments. Behind the scenes, every operation is powered by direct Snowflake SQL or Snowpark Python functions, offering unparalleled flexibility in deploying predictive workloads within Snowflake.

This setup ensures that data remains securely within the Snowflake data platform. It exemplifies the concept of bringing code to the data rather than vice versa, a paradigm shift enabled by Snowflake with numerous advantages:

• Enhanced security as data never leaves Snowflake.
• Mitigation of unnecessary ingress/egress costs.
• Streamlined billing encompassing both data storage and ML workloads, alongside opportunities to optimize ML compute costs through Snowflake's warehouse configuration options.

Traditionally, a significant portion of data science involves data wrangling and cleaning. Snowflake, as a data warehouse, provides an excellent environment for such development activities. Now, with the ability to seamlessly integrate predictive workloads within the platform, we unlock substantial development efficiencies. By eliminating the need to transfer data across various tools and systems, we facilitate smoother workflows and maximize the utility of our data resources.