Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment
Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, laying the foundation for building a fraud detection model without writing code.
In the third and final part of this series, the focus is on visualizing insights from fraud detection models built using Amazon SageMaker Canvas, and integrating those insights into interactive dashboards using Amazon Quick Sight. The process begins by importing the Canvas predictions as a dataset directly within Amazon Quick Sight, creating a foundation for analysis and visualization.
Users then craft visualizations and leverage generative BI capabilities to uncover patterns and trends within the data, all without needing additional infrastructure or custom integrations. This seamless integration from ML predictions to business-ready dashboards is a key advantage of using Amazon Quick Sight in tandem with Amazon SageMaker Canvas.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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