Articles describe a three-part no-code workflow using Snowflake, Amazon SageMaker Canvas, and Amazon QuickSight to build and publish a fraud-detection dashboard with AI-generated executive summaries.
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The coverage outlines how to set up a Snowflake environment with sample fraud data, connect it to Amazon SageMaker Canvas to prepare data and train a model, and then import predictions into Amazon QuickSight to create an interactive fraud-detection dashboard. The dashboard publication steps include enabling AI-generated executive summaries and supporting sharing, scheduled delivery, and threshold alerts.
Amazon QuickSight
Amazon SageMaker Canvas
Snowflake
AWS
Amazon Quick
Amazon Quick Sight
Amazon Quick – Part 1: Setting up your Snowflake environment
Amazon SageMaker
Amazon SageMaker Data Wrangler
FRAUD.PUBLIC.FRAUD_TABLE
Generative BI
Snowflake fraud dataset
XGBoost
fraud-detection dashboard
Source coverage
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight
Amazon Web Services
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
Amazon Web Services
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment
Amazon Web Services