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
As Head of AI/ML Strategy at Snowflake, Ahmad leverages his expertise in AI/ML to help customers optimize their workloads on Snowflake. Working closely with the Snowflake product team, he helps define the ML feature set within Snowflake based on customer feedback. Prior to joining Snowflake, Ahmad spent over 4 years at AWS where he focused on the AWS stack of ML services, and was involved in early proof of concepts for AWS SageMaker. Ahmad holds a Masters in Electrical & Computer Engineering from the University of Southern California.