This two-part series details how to build a no-code machine learning workflow using Snowflake, Amazon SageMaker Canvas, and Amazon QuickSight. The first part focuses on setting up the AWS environment and connecting to Snowflake, highlighting the challenges healthcare, retail, and life sciences teams face in leveraging their data for predictions. The second part guides users through data preparation using Data Wrangler, joining transaction data, and training an XGBoost model for fraud detection within Amazon SageMaker Canvas. AI
IMPACT Enables non-technical users to build ML models, potentially accelerating adoption in data-rich industries.
RANK_REASON The cluster describes a tutorial on using existing products to build an ML workflow, not a new product release.
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