Seamlessly transition between no-code and code-first machine learning with Amazon SageMaker Canvas and Amazon SageMaker Studio

The article discusses how to seamlessly transition between no-code and code-first machine learning using Amazon SageMaker Canvas and Amazon SageMaker Studio.

Specifically, the article covers:

• An overview of SageMaker Canvas, a no-code ML tool for business teams, and SageMaker Studio, an IDE for code-first ML development
• Two options for sharing models between SageMaker Canvas and SageMaker Studio:
  1. SageMaker Model Registry: Registering models from Canvas to a model registry for review and approval by ML experts before deployment
  2. Notebook export: Exporting notebooks from Canvas for ML experts to import and customize in SageMaker Studio
• Detailed steps for reviewing, approving, and deploying models using each option
• Conclusion highlighting the benefits of collaboration and transitioning between no-code and code-first ML environments