Train, Deploy, and Run a ML model using Python, Snowpark and Streamlit

In this session, we will train a Linear Regression model to predict future ROI (Return On Investment) of variable advertising spend budgets across multiple channels including search, video, social media, and email using Snowpark for Python and scikit-learn. By the end of the session, you will have an interactive web application deployed visualizing the ROI of different allocated advertising spend budgets.

During this hands-on session, we will:

- Set up your favorite IDE (e.g. Jupyter, Visual Studio Code) for Snowpark and ML
- Analyze data and perform data engineering tasks using Snowpark DataFrames
- Use open-source Python libraries from a curated Anaconda channel with near-zero maintenance or overhead
- Deploy ML model training code to Snowflake using Python Stored Procedures
- Create and register Python User-Defined Functions (UDFs) for inference
- Create Streamlit web application that uses the UDF for real-time prediction based on user input