Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks

Learn how to build and register Snowpark ML models with Streamlit in Snowflake and output code in a Snowflake notebook!

Link to demo video Link to Quickstart

Solution Innovation

overview

This solution helps you quickly iterate the model building process with a Streamlit in Snowflake application that generates Snowpark ML code:

Create and register Snowpark ML models with a few clicks.
Run inference on your test data with the Model Registry.
Generate a Snowflake or Jupyter notebook with Snowpark ML code to build and refine the models using additional parameters.

Snowflake Features Used

Build this solution now Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks

Solution Architecture: Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks

SiS app for building and deploying Snowpark ML models
Once models are built, you can see the registered models in Model Registry
You can also output to a Snowflake or jupyter notebook with the same Snowpark ML code that built the model within the app

HANDS-ON TECHNICAL RESOURCES

Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks [quickstart]

Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks [video]

Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks [github]

Play with Source Code

Build and Deploy Snowpark ML Models using Streamlit and Snowflake Notebooks [blog]