Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick

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 business-ready dashboards without requiring additional infrastructure or custom integrations.

This section walks through the complete workflow for visualizing fraud detection predictions in Amazon Quick Sight, from importing your Amazon SageMaker Canvas predictions as a dataset to building interactive dashboards and leveraging generative BI capabilities for natural language insights.

Before starting Part 3, complete the following:

Before building visualizations, import the Canvas predictions into Amazon Quick Sight as a dataset:

With the dataset imported, the next step is creating visuals to analyze the predictions:

Figure 1: Fraud detection analysis dashboard showing fraud patterns across transaction categories, merchant behavior, and temporal trends

To access Generative BI capabilities in Amazon Quick Sight, upgrade your account’s users to Admin Pro, Author Pro, or Reader Pro roles within your Amazon Quick subscription. Pro roles grant access to the generative BI capabilities relevant to each assigned role, including the ability to share generative Q&A topics with other users. To understand how subscription names map to user roles, see Understanding Amazon Quick subscriptions and roles.

Figure 2: Amazon Quick Sight generative BI pane with context-aware options for building calculations, visuals, and Q&A topics

Figure 3: Generated visual of fraud rate by transaction hour created from a natural language query

Figure 4: Generative BI answer returning total fraud cases in Washington state from a natural language question

To publish the dashboard, complete the following steps:

Figure 5: Publishing an analysis in Amazon Quick Sight using the Publish option

Figure 6: Publish dashboard dialog with a dashboard name and the Allow executive summary option selected

Figure 7: From the published dashboard, users can generate an executive summary using the Create option

Figure 8: AI-generated executive summary providing natural language insights from the dashboard

The fraud detection dashboard brings together Amazon SageMaker Canvas predictions and Amazon Quick Sight to surface fraud patterns across transaction categories, merchant behavior, and temporal trends all in one interactive view. The published dashboard further enables automated report scheduling, threshold-based alerts, and AI-generated executive summaries, empowering both technical and non-technical audiences to make faster, data-driven decisions.

With generative BI, stakeholders can query insights in natural language and access AI-generated executive summaries, closing the loop on the end-to-end no-code ML workflow built across this series.

This three-part series demonstrates how AWS services can work together to take organizations from raw data to actionable business intelligence entirely through visual, no-code tooling.

For organizations managing large volumes of operational data, this workflow removes the traditional barriers between data science and business decision-making. Insights that once required specialized engineering resources are now accessible to analysts, operations teams, and executives through a governed, scalable, and fully managed AWS architecture. Whether the goal is detecting anomalies, forecasting demand, classifying risk, or understanding customer behavior, this approach scales across use cases and industries.

To start transforming your Snowflake data into actionable business intelligence, explore Amazon SageMaker Canvas and Amazon Quick Sight.