Turbocharging AI/ML with the Snowflake Data Cloud
Present how Snowflake accelerates AI/ML innovation through: 1. Central and governed access to data. 2. Reliably process features, ML models and applications using elastic compute engine. 3. Bring Generative AI/LLM capabilities within Snowflake's secure and governed boundary.
Bringing AI/ML models into production takes three key stages: the development of the model, the operations around running the model in production and then bringing that model into an application where users can either consume model results or interact with the model itself and get insights on-demand.
Snowflake accelerates AI/ML innovation through following ways:
1. Central and governed access to data from your organization and partners/Snowflake Marketplace.
2. Reliably process features, ML models and applications using elastic compute engine without having to move data to external, ungoverned environments helping bring AI/ML to the data.
3. Bringing Generative AI/LLM capabilities within Snowflake's secure and governed boundary.
This session will provide a deep dive into Snowflake's AI/ML capabilities with a focus on:
1. Using Snowpark for AI/ML
2. Snowpark Container Services for governed Generative AI/LLMs
3. Streamlit in Snowflake for intelligent apps
4. ML Powered Functions