本系列共三部分，详细介绍了如何使用 AWS 服务构建无代码机器学习工作流。第一部分侧重于设置必要的 Snowflake 和 AWS 环境。第二部分指导用户使用 Amazon SageMaker Canvas 进行数据准备和模型构建，利用 Data Wrangler 和 XGBoost 算法等工具进行欺诈检测。最后一部分解释了如何使用 Amazon Quick Sight 可视化这些机器学习预测，并将它们集成到交互式仪表板中以实现商业智能。 AI
影响 通过使业务用户无需编码专业知识即可构建预测模型，从而实现机器学习的民主化。
排序理由 详细介绍多个 AWS 服务集成以实现特定用例的博客系列。
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Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, layin…
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 int…
In Part 3 of this no-code ML series, you bring fraud detection predictions to life. Import your Amazon SageMaker Canvas predictions into Amazon Quick Sight, build interactive dashboards, use generative BI to answer questions in natural language, and publish AI-generated executive…
🤖 Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 1: Setting up your Snowflake environment Healthcare, retail, and life sciences teams store large volumes of operational data in Snowflake, but turning it into predictions is hard. In Par…
🤖 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 da…