Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data…
AWS Machine Learning Blog reports: Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 2: Data preparation and model…
By Dillip Chowdary • Aug 23, 2026 • Source: AWS Machine Learning Blog
What happened
AWS Machine Learning Blog reports: 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. 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 in Part 3.
Advertisement
Tech Pulse Daily
Get tomorrow's pulse first
Join engineers who read Tech Pulse before stand-up. Free, weekday mornings.
How it works
Read the original coverage at AWS Machine Learning Blog via the source link above for the complete details and primary quotes.
Who is affected
Cross-check release notes and official docs before changing production systems based on early reporting.
Developer Action Items
- ☐ Diff the official changelog for Amazon / AWS before you bump — APIs, defaults, and removed flags only.
- ☐ Install through the vendor's documented channel in staging; keep a one-command rollback and time-box the canary.
- ☐ Grep your repo for old flag names, lockfile pins, and plugin versions that the notes mark as breaking.
- ☐ Prefer the first patch cut over the day-zero tag unless you have a reason to be on the leading edge.
- ☐ If AWS Machine Learning Blog did not name a region, plan, or SKU, screenshot the official availability line before you promise it to users.
Advertisement