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

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 Sightis a...

Copyright of this story solely belongs to amazon.com. To see the full text click HERE