Amazon SageMaker Feature Store now supports SageMaker Python SDK V3

Amazon SageMaker Feature Store now supports the SageMaker Python SDK v3, including new capabilities for Lake Formation access controls and Apache Iceberg table properties configuration. Feature Store is a fully managed repository to store, share, and manage features for machine learning models. Data scientists can now use the modern, modular SDK v3 interfaces to manage feature groups with fine-grained access control and optimized offline storage. Data scientists can use the SageMaker Python SDK v3 to manage feature groups with streamlined workflows and reduced boilerplate. With Lake Formation integration, data scientists can enforce column-level and row-level access control on offline store data through an opt-in setting at feature group creation. With Iceberg properties support, data scientists can configure additional table properties such as compaction and snapshot expiration directly through the SDK to optimize storage and query performance. These capabilities allow data scientists to govern access to feature data and optimize offline store performance from a single SDK without managing separate tools. These capabilities are available in all [AWS Regions](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) where[ Amazon SageMaker Feature Store](https://aws.amazon.com/sagemaker/ai/feature-store/) is available. To get started, install [SageMaker Python SDK v3.8.0](https://sagemaker.readthedocs.io/en/stable/) or later. For more information, see [Lake Formation access controls ](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-lf-governance.html)and[ Iceberg metadata management](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-iceberg-metadata-management.html) documentation.