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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Part 1 covered the Snowflake database implementation setup and established the foundational infrastructure for our no-code machine learning (ML) workflow. Part 2 walked through the 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 visual transformations, and build a fraud detection model using the XGBoost algorithm. In Part 3, the workflow comes full circle by integrating SageMaker Canvas predictions with Amazon Quick Sight, now part of Amazon Quick, to create interactive dashboards that combine operational data with ML predictions for fraud detection business intelligence (BI). This post covers how to import Canvas predictions into Amazon Quick Sight as a dataset, build an analysis dashboard, use generative BI capabilities to surface insights through natural language, and publish those insights to stakeholders. Building dashboards with Amazon Quick Sight Amazon Quick Sight is a powerful business intelligence service within Amazon Quick that teams can use to build interactive dashboards, perform deep data analysis, and share insights across their organization. Amazon Quick extends these capabilities with generative AI that supports natural language queries over your data, custom AI agents for workflow automation, and collaborative Spaces for organizing files, dashboards, and knowledge bases in one place. For teams already using Amazon SageMaker Canvas, Amazon Quick provides a direct path from ML predictions to …

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