Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment
Amazon Web Services Anu Kaggadasapura Nagaraja ● Covered by 3 sources
The post sets up a Snowflake environment as the first step in a three-part no-code ML workflow using Amazon SageMaker Canvas and Amazon QuickSight, including creating a fraud database, loading sample data, and generating the Snowflake connection name. It creates the sample fraud dataset by inserting 139538 rows into FRAUD.PUBLIC.FRAUD_TABLE. With the environment configured and connection details retrieved, the next part can connect SageMaker Canvas to Snowflake to prepare data and build a fraud detection model.
Why it matters
Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, laying the foundation for building a fraud detection model without writing code.