Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model building with Amazon SageMaker Canvas
SageMaker Canvas + Snowflake: no-code fraud detection for practitioners who don't write ML code.
Why it matters
A practical tutorial on building production ML workflows without coding, using AWS managed tools and Snowflake. Relevant for data teams evaluating no-code ML platforms and practitioners looking to operationalize fraud detection quickly.
The key facts
11 to knowPart 2 of multi-part series on no-code ML workflows
Integration: Amazon SageMaker Canvas + Snowflake
Use case: XGBoost fraud detection model
Tooling: Data Wrangler for visual transformations, SageMaker Canvas for training
No ML code required for data prep or model building
Series culminates in Part 3 with interactive dashboards
SageMaker Canvas integration with Snowflake for data access
Visual data transformation workflow using Data Wrangler
XGBoost fraud detection model training without code
Multi-part tutorial (Part 2 of 3) focused on data prep and model building
End-to-end workflow: Snowflake → Canvas → Dashboard
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: In Part 2 of this no-code ML series, you connect Amazon SageMaker Canvas to Snowflake, prepare and join transaction data with Data Wrangler visual transformations, and train an XGBoost fraud detection model. All without writing machine learning code, laying the groundwork for interactive dashboards…