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ExternalAI
AWS Machine Learning Blog

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 Part 1 of this series, you set up your AWS account and Snowflake environment for a no-code ML workflow with Amazon SageMaker Canvas, laying the foundation for building a fraud detection model without writing code.

Advanced (300)Amazon SageMaker CanvasTechnical How-to
AWS Machine Learning BlogRead original
ExternalAI
AWS Machine Learning Blog

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.

Advanced (300)Amazon SageMaker CanvasTechnical How-to
AWS Machine Learning BlogRead original
ExternalAI
AWS Machine Learning Blog

Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight

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 summaries for stakeholders.

Advanced (300)Amazon Quick SightAmazon SageMaker Canvas
AWS Machine Learning BlogRead original
ExternalCloud
DigitalOcean Blog

DigitalOcean Inference Router, Now Cache-Aware: Why the Cheapest Model Isn't Always the Best Deal

Coinbase CEO Brian Armstrong recently posed the question every company scaling AI is asking: how do you keep spend flat while token usage grows exponentially? This isn’t hypothetical. It’s confronting companies across every sector: Uber exhausted its annual AI coding budget within the first four months of the year and subsequently introduced a $1,500 monthly limit per employee. Walmart placed token limits on its internal Code Puppy agent after employees repeatedly asked it to solve similar probl

DigitalOcean BlogRead original

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