No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Versioning

The video tutorial explains how to create and manage different versions of a project using SageMaker Canvas. It uses a banknote authentication project as an example, demonstrating how to modify data columns and observe changes in model accuracy. The tutorial emphasizes that while you can change data columns, the target column must remain the same. It highlights the importance of versioning in comparing model performance and improving project outcomes.

What is the purpose of creating different versions in a project?

How does removing certain columns from the dataset affect the accuracy of the model?

What limitations exist when creating a new version in a project?

Describe the process of comparing accuracy between different versions.

What is the significance of the target column when creating versions?