Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment

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Healthcare, retail, and life sciences organizations generate massive quantities of operational data in cloud data warehouses like Snowflake. While these systems store and scale information efficiently, transforming that data into meaningful predictions remains a challenge. Traditional machine learning (ML) approaches require specialized teams, long development cycles, and heavy engineering support, creating delays and limiting experimentation for the business users who understand the data best. A no-code ML workflow changes that dynamic. With Amazon SageMaker Canvas, you can explore datasets, prepare features, build predictive models, and generate insights visually without writing code and without depending on data science resources. Business analysts, product owners, and operational teams can accelerate decision-making while maintaining enterprise security and governance. This is Part 1 of a three-part series. Part 1 covers setting up your AWS account and Snowflake environment. Part 2 connects Amazon SageMaker Canvas to Snowflake to prepare data and build a fraud detection model. Part 3 sends predictions to Amazon Quick to create interactive dashboards and share insights with stakeholders. Business challenge This solution was inspired by a real healthcare organization that had accumulated years of operational data in Snowflake including sales transactions, product movement, patient interactions, and regional performance metrics. While the data foundation was robust, turning that data into predictive insights remained a challenge. Business teams wanted to forecast …

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• Originalquelle / Hersteller-Advisory lesen: https://aws.amazon.com/blogs/machine-learning/build-a-no-code-ml-workflow-with-snowflake-amazon-sagemaker-canvas-and-amazon-quick-part-1-setting-up-your-snowflake-environment/
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