No-Code ML Workflow: Connect SageMaker Canvas to Snowflake and Train a Fraud Model

Part 2 shows no-code fraud model training using SageMaker Canvas, Snowflake, and Data Wrangler.

What happened

This second installment of the no-code machine learning series links Amazon SageMaker Canvas to Snowflake as the data foundation.

Transaction data is prepared and joined using Data Wrangler's visual transformations, with no code required.

An XGBoost fraud detection model is trained through the same visual workflow, setting the stage for a later dashboard phase.

The workflow demonstrates that meaningful machine learning tasks, such as fraud detection, can be completed entirely through visual interfaces.

By removing coding requirements, the approach makes model building accessible to a wider range of practitioners.

Amazon SageMaker Canvas is connected to Snowflake.

Data Wrangler visual transformations prepare and join transaction data.

An XGBoost fraud detection model is trained without writing machine learning code.

Part 3 in the series is positioned to build interactive dashboards on top of the prepared and modeled data.

The series highlights how a full no-code ML pipeline can be assembled using separate AWS services in combination.