Due to data security risks and infrastructure scalability limitations, machine learning (ML) models often never make it to production. And many of those that do are inaccessible to the business users that can generate value from forecasts, predictions, user segmentations, and more. By using Snowpark for Python to build and deploy models inside Snowflake’s secure and elastic engine, and Streamlit to build business-friendly data applications, organizations can generate more value from their ML initiatives.
Join us to learn how to:
- Effortlessly go from development to production using Python running in Snowflake with Snowpark
- Build interactive business applications using only Python with Streamlit
- Take action in your organization with Snowpark and Streamlit resources
Senior PMM, Snowflake
Senior Developer Advocate, Snowflake