ML Sidekick, a no-code app built using Streamlit in Snowflake, designed for building and deploying machine learning models in Snowflake. This application aids both seasoned data scientists and business users with no coding experience by simplifying the machine learning process and making it accessible to a broader audience. This applications provides features for:

• Selection and preprocessing of data to build machine learning models
• Training and evaluation machine learning models within the Snowflake environment
• Logging models to Snowflake model registry
• Generation python code for the pipeline in form a notebook
• Exploration/comparison different versions of registered models or different models

For prerequisites, environment setup, step-by-step guide and instructions, please refer to the QuickStart Guide

All sample code is provided for reference purposes only. Please note that this code is provided as is and without warranty. Snowflake will not offer any support for the use of the sample code. The purpose of the code is to provide customers with easy access to innovative ideas that have been built to accelerate customers' adoption of key Snowflake features. We certainly look for customers' feedback on these solutions and will be updating features, fixing bugs, and releasing new solutions on a regular basis.

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