Amazon SageMaker Studio is a web-based fully integrated development environment (IDE) where you can perform end-to-end machine learning (ML) development to prepare data and build, train, and deploy models.
Like other AWS services, Studio supports a rich set of security-related features that allow you to build highly secure and compliant environments.
One of these fundamental security features allows you to launch Studio in your own Amazon Virtual Private Cloud (Amazon VPC). This allows you to control, monitor, and inspect network traffic within and outside your VPC using standard AWS networking and security capabilities. For more information, see Securing Amazon SageMaker Studio connectivity using a private VPC.
Customers in regulated industries, such as financial services, often don’t allow any internet access in ML environments. They often use only VPC endpoints for AWS services, and connect only to private source code repositories in which all libraries have been vetted both in terms

Continue reading