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
Source: AWS Machine Learning (opens in a new tab) · Anu Kaggadasapura Nagaraja
Intel Summary
Amazon Web Services published a technical guide detailing how to build an end-to-end, no-code machine learning pipeline by integrating Snowflake with Amazon SageMaker Canvas. The tutorial demonstrates how non-technical users and data analysts can ingest and transform transactional data using visual data preparation tools, and subsequently train an XGBoost model for fraud detection without writing custom code. The guide outlines the workflow needed to prepare datasets before deploying downstream analytics and visualization dashboards.
Why It Matters
Enterprises increasingly seek ways to bridge data warehouses like Snowflake with machine learning environments without requiring dedicated data science teams for standard tabular tasks. By streamlining data preparation and model training within a visual interface, organizations can accelerate proof-of-concept deployments for use cases such as fraud detection, lowering the barrier to entry for enterprise ML adoption across business analytics teams.
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