Integrating custom dependencies in Amazon SageMaker Canvas workflows

The article describes how to integrate custom dependencies into Amazon SageMaker Canvas workflows, demonstrating how to use external libraries not natively supported by the platform.

• Details a workflow for incorporating custom scripts and dependencies stored in Amazon S3
• Provides a step-by-step guide to packaging custom code and dependencies in a zip file
• Shows how to dynamically load and execute custom functions with external libraries like mpmath
• Demonstrates data transformation and model training using a shipping delivery prediction example
• Highlights the ability to extend SageMaker Canvas capabilities beyond its 300+ built-in functions

The solution enables data scientists to use custom code and libraries in SageMaker Canvas, expanding its no-code/low-code machine learning capabilities.