This AWS Machine Learning Blog post is the second installment of a no-code machine learning series. It describes how to connect Amazon SageMaker Canvas to Snowflake, then prepare and join transaction data using Data Wrangler visual transformations. The tutorial then covers training an XGBoost model for fraud detection, all without writing machine learning code. The post also notes that this work prepares for interactive dashboards, which are addressed in Part 3.
Why it matters
The walkthrough demonstrates a workflow for building a fraud detection model without hand-coding, potentially lowering the technical barrier for data preparation and model training.
Who should care
Teams using Snowflake and AWS tooling who want to prepare data and build models through a no-code interface may find the step-by-step approach relevant.