Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas

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Part 1 covered the Snowflake database setup and established the foundational infrastructure for this no-code machine learning (ML) workflow. Part 2 of this blog series covers complete data preparation and model building workflow using Amazon SageMaker Canvas, demonstrating how to connect directly to Snowflake data sources, transform and prepare data using Data Wrangler’s visual transformations, and build a fraud detection model using the XGBoost algorithm. Amazon SageMaker Canvas is a visual, no-code machine learning service that enables business analysts and domain experts to build accurate ML models and generate predictions. Amazon SageMaker Canvas provides an intuitive interface for data preparation, model training, and prediction generation democratizing access to machine learning across organizations while maintaining enterprise security and governance. Solution overview This solution guides you through the complete workflow of preparing data and building a machine learning model using Amazon SageMaker Canvas, with direct integration to your Snowflake data warehouse. Prerequisites Complete Part 1 setting up your Snowflake environment before starting this post. You need the Snowflake account credentials and connection details from Part 1 to complete the steps in this post. Amazon SageMaker Canvas setup Open the AWS Management Console and search for Amazon SageMaker Canvas. Select it from the list, or press Enter. Create your Amazon SageMaker domain A domain provides the foundational organizational unit for your Amazon SageMaker environment. It serves as a …

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