Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment
No-code ML just got easier: Snowflake + SageMaker Canvas eliminate the coding barrier for fraud detection and predictions.
Why it matters
A practical tutorial on building ML workflows without code using AWS and Snowflake—useful for practitioners who want to move data to predictions without hiring data scientists, but limited novelty (existing products, setup guide).
The key facts
11 to knowAmazon SageMaker Canvas (no-code ML)
Snowflake integration
Use case: fraud detection
Industries: healthcare, retail, life sciences
Tutorial/setup-focused content
Part 1 of multi-part series
Amazon SageMaker Canvas (no-code ML platform)
Snowflake integration for operational data
Use case: fraud detection model
Target industries: healthcare, retail, life sciences
Part 1 of multi-part series (foundational setup)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: 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…