Building a No-Code Machine Learning Workflow with Snowflake and Amazon SageMaker Canvas

Part 2 of this series demonstrates how to connect Snowflake to Amazon SageMaker Canvas for a complete, no-code machine learning workflow. It begins with establishing the environment, including setting up the SageMaker domain and user profile, then proceeds to connect Snowflake as a data source within Data Wrangler. Users can then transform and prepare data visually, including joining multiple datasets, creating features, and removing sensitive information.

The process continues with data quality analysis, which helps identify issues like class imbalance or target leakage, and then moves to model training. Using the XGBoost algorithm within an ensemble method, the model is trained and evaluated through detailed metrics and feature importance insights. Once trained, the model can generate predictions on new data, which can be visualized in Amazon Quick Sight, provided the necessary permissions and configurations are in place.

This workflow enables business analysts and domain experts to build and deploy ML models efficiently, without requiring programming skills, while maintaining enterprise security and governance. The article concludes by setting the stage for Part 3, which will focus on visualizing these insights through interactive dashboards.