Virtual Private Cloud (VPC) support is generally available for SageMaker Ground Truth
Amazon SageMaker Ground Truth helps you build high-quality training datasets for your machine learning (ML) models. With SageMaker Ground Truth, you can use workers from Amazon Mechanical Turk, a vendor company that you choose, or your own private workforce to create labeled datasets for training ML models.
Amazon SageMaker Ground Truth helps you build high-quality training datasets for your machine learning (ML) models. With SageMaker Ground Truth, you can use workers from Amazon Mechanical Turk, a vendor company that you choose, or your own private workforce to create labeled datasets for training ML models.
Starting today, you can now use SageMaker Ground Truth to create and run a labeling job inside an Amazon Virtual Private Cloud (VPC), instead of connecting over the internet. This allows you to use Ground Truth while keeping your data in S3 buckets that are logically isolated and secure in your Amazon VPC.
When you launch a labeling job or access a Amazon SageMaker Ground Truth private worker portal with an Amazon VPC, you have full control over your network environment. You can conduct communication between your VPC and SageMaker Ground Truth entirely and securely within the AWS network. You can also control SageMaker Ground Truth’s access to VPC-restricted S3, or launch automated data labeling jobs in a VPC. In addition, SageMaker Ground Truth can interact with your pre-annotation and post-annotation AWS Lambda functions using an AWS PrivateLink endpoint.
To get started with Amazon SageMaker Ground Truth and to create a SageMaker Ground Truth labeling job in VPC, refer to the documentation or visit the product page.