AWS Tutorial: No-Code Fraud Detection with SageMaker Canvas on Snowflake
AWS ML Blog · rss · 2026-08-21
AWS ML Blog kicks off a three-part tutorial on building a no-code ML workflow with Amazon SageMaker Canvas, letting business analysts build predictive models directly on Snowflake data.
- The motivating case is a healthcare organization with years of operational data but too little data science capacity, where every prediction request required engineering support.
- Architecture: Snowflake data → visual model building in Canvas → deploy to SageMaker Endpoint → batch predictions to S3 → interactive dashboards in Amazon Quick Sight.
- Claimed benefits: 300+ visual Data Wrangler transformations, model development cut from months to hours, support for regression, classification and time-series forecasting.
- Part 1 provides copy-paste SQL for creating the Snowflake database and generating simulated 2020 fraud transaction data.
More from Apps
- AI Security System Fail: Not Replacing Watchdogs Yet — AIandDesign · 2026-08-21
- Turning any livestream into a prediction market — moonsandhues · 2026-08-21
- First Impression of Grok Bot: Actually Useful Proactive Follow-up Messages — GregKamradt · 2026-08-21
- ChatGPT can now send texts for you with new Apple Messages plugin — TechCrunch AI · 2026-08-21
- Building an AI-native email client on Cloudflare Email, Workers and AI Gateway — ritakozlov · 2026-08-21
- Musk shares the easiest way to try Grok Build — a web link, no scripts or downloads — elonmusk · 2026-08-21