Amazon SageMaker Unified Studio now supports Amazon Virtual Private Cloud (Amazon VPC) for notebook kernels. With this launch, notebook kernels execute within the VPC configured at the domain level, giving enterprises network isolation for interactive data and machine learning (ML) workloads. This helps customers meet security and compliance requirements by keeping applicable notebook compute traffic within their VPC boundaries. With VPC support for notebook kernels, data engineers, analysts, and data scientists can connect to private resources from their notebooks. The notebook kernel inherits the VPC settings, subnets, and security groups defined at the SageMaker Unified Studio domain level, so administrators can manage network policies centrally. This means you can query private databases, access internal APIs, and work with data sources that are not publicly accessible, all from the same notebook environment that supports SQL, Python, and natural language through the built-in data agent. This VPC configuration only applies to the notebook's interactive compute, where your Python code and dataframes execute. For VPC configurations with other compute engines, refer to the documentation for each individual engine. You can use VPC-enabled notebook kernels in all AWS Regions where Amazon SageMaker Unified Studio is supported . To learn more, see the SageMaker Unified Studio user guide and the Amazon SageMaker product page.