Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight
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This is Part 3 of a series on building a no-code ML workflow using Snowflake, Amazon SageMaker Canvas, and Amazon Quick Sight (now part of Amazon Quick). It explains how to import Canvas fraud detection predictions into Amazon Quick Sight as a dataset and create an analysis dashboard. The dashboard visualizes fraud patterns across transaction categories, merchant behavior, and temporal trends. Generative BI capabilities allow users to build visuals and get answers through natural language queries, with examples like fraud rate by transaction hour and total fraud cases in Washington state. After publishing, users can share dashboards, schedule report delivery, set threshold alerts, export PDFs, and generate AI-powered executive summaries. The series concludes that this workflow removes barriers between data science and business decision-making, making insights accessible through managed AWS services without additional infrastructure.
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