From data to ML: Building an end-to-end machine learning pipeline with Aporia on Snowflake

This tutorial demonstrates how to build a robust end-to-end machine learning pipeline using Snowflake's Snowpark and Aporia. The process includes training and deploying models, storing inference data in the Snowflake Data Cloud, and integrating Aporia for ML observability, monitoring, and improving model performance in production. By leveraging these tools, users can efficiently train and deploy models in Snowpark while monitoring and managing their production models with Aporia, enabling continuous improvement of their machine learning pipeline.