Amazon SageMaker Canvas offers users end-to-end machine learning (ML) applications without the need for coding. The interface supports functionalities such as data preparation, endpoint deployment, and adjustment of foundation models for generative AI among others.
A crucial step in enabling users and ensuring secure environments involves the use of single sign-on (SSO) via AWS IAM Identity Center, a service that manages user access to AWS resources. Configuration of SSO on SageMaker Canvas involves creating or connecting workforce users and managing their access to all AWS accounts and applications.
The use of IAM Identity Center for SSO configuration includes adding users or groups to the SageMaker Studio domain. This allows users to access SageMaker Canvas with rewarding results, including seamless access without the necessity of first accessing the AWS Management Console.
To access SageMaker Canvas, users must follow the steps outlined in the blog post, including enabling IAM Identity Center using AWS Organizations, creating a SageMaker Studio domain with IAM Identity Center for user authentication, creating users in IAM Identity Center, and adding users to the SageMaker Studio domain.
Additionally, it’s beneficial to rename the SageMaker Studio application to SageMaker Canvas for clear identification. Providing access to SageMaker Canvas involves the creation of users or groups given access to the program. It may be more straightforward to use a user group, a collection of users, for simplicity in specifying permissions.
Consequently, SageMaker Canvas is accessed through an email link received by the user. They have three options to log in to SageMaker Canvas: accessing from SageMaker Studio through the IAM Identity Center portal, accessing from SageMaker Canvas through the IAM Identity Center portal, or using the IAM Identity Center portal link.
In conclusion, the IAM Identity Center makes it easy for users to securely access SageMaker Canvas using SSO. By configuring the center and linking it to the SageMaker domain where SageMaker Canvas is used, users gain simplified access to the application and its no-code ML capabilities.
Developers Dhiraj Thakur and Dan Sinnreich have reimagined access to ML for a wider audience through SageMaker Canvas in an effort to democratize technology. The result is a simple and accessible interface that gives users the agility to independently build ML tools while ensuring a secure environment.
Amazon continues to innovate by providing services that reduce the barriers to technology adoption and promote collaborative development. These advancements in ML represent a concerted effort on the part of Amazon to broaden the reach of its technological offerings, with the ultimate goal of making ML more accessible to businesses and consumers.
