This guide walks you through an end-to-end customer segmentation machine-learning use-case using Snowpark for Python, Snowflake Feature-Store and Model-Registry, and Snowpark ML. The primary focus in this guide is the Snowflake Feature Stores functionality and how it integrates within the broader ML eco-system within Snowflake.
By completing this guide, you will be able to go from ingesting raw data through to implementing a production inference data-pipeline to maintain customer segments.
Here is a summary of what you will be able to learn in each step by following this quickstart:
- Setup Environment: Use stages and tables to ingest and organize raw data from S3 into Snowflake tables. Setup a scheduled process to simulate incremental data-ingest into Snowflake tables.
- Feature Engineering: Leverage Snowpark for Python DataFrames to perform data cleansing, transformations such as group by, aggregate, pivot, and join to create features the data for machine-learning.
- Feature Store: Use Snowflakes feature-store to register and maintain feature-engineering pipelines and understand how to monitor them once operational.
- Machine Learning: Perform feature transformation and run ML Training in Snowflake using Snowpark ML. Register the trained ML model for inference from Snowpark ML Model Registry
- Operationalise a Model: Implementing a production inference data-pipeline to maintain customer segments as underlying customer behaviours change in source data.
The diagram below provides an overview of what we will be building in this QuickStart.
For prerequisites, environment setup, step-by-step guide and instructions, please refer to the QuickStart Guide.