Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment
TL;DR
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.
Nauti's Take
The advantage is obvious: business teams can build a fraud model in Canvas without waiting on a data science queue. The catch is the bill underneath, since Snowflake plus SageMaker plus data transfer adds up fast, and part one is pure setup with no model in sight.
Sensible for teams already living in Snowflake, while everyone else is better served by a leaner stack.
Sources
