All news with #sagemaker tag

Amazon SageMaker Unified Studio Adds Catalog Notifications

🔔 Amazon SageMaker Unified Studio now delivers real-time notifications for data catalog activities, including new dataset publications, metadata changes, subscription requests, comments, and access approvals. Alerts are surfaced via a bell icon on the project home page and through a notification center that shows a recent list and a full, filterable tabular view by catalog, project, and event type. The feature is available in all regions where SageMaker Unified Studio is supported.

Aurora PostgreSQL zero-ETL now integrates SageMaker

🔁 Amazon Aurora PostgreSQL-Compatible Edition now offers zero-ETL integration with Amazon SageMaker, enabling near-real-time replication of PostgreSQL tables into a lakehouse. The synced data conforms to Apache Iceberg open standards and is immediately accessible to SQL, Apache Spark, BI, and ML tools via a simple no-code interface without impacting production workloads. Comprehensive, fine-grained access controls are enforced across analytics engines, and the capability is available in multiple AWS Regions.

SageMaker AI Projects Adds Custom ML Templates from S3

🛠️ Amazon Web Services announced that SageMaker AI Projects can now provision custom ML project templates stored in Amazon S3. Administrators can define and manage standardized end-to-end project templates in SageMaker AI Studio so data scientists can create projects that follow organizational patterns and automated workflows. The feature is available in all AWS Regions where SageMaker AI Projects is offered.

SageMaker Unified Studio adds SSO for Spark sessions

🔐 Amazon SageMaker Unified Studio now supports corporate identities for interactive Apache Spark sessions using AWS Identity Center trusted identity propagation. Data engineers and scientists can sign on to JupyterLab Spark sessions with organizational credentials while administrators apply fine-grained access controls and maintain end-to-end data access traceability. The integration leverages AWS Lake Formation, Amazon S3 Access Grants, and Amazon Redshift Data APIs, and includes comprehensive AWS CloudTrail logging for interactive and background sessions to streamline compliance.

Amazon SageMaker HyperPod Adds Managed Karpenter Autoscaling

🛠️ Amazon SageMaker HyperPod now supports managed node autoscaling using Karpenter, enabling automated cluster scaling for both inference and training workloads. This managed capability removes the operational burden of installing and maintaining autoscaling infrastructure while providing integrated resilience and fault tolerance. Customers gain just-in-time GPU provisioning, scale-to-zero during low demand, workload-aware instance selection, and cost reductions through intelligent consolidation.

Amazon SageMaker Adds EC2 P6-B200 Notebook Instances

🚀 Amazon Web Services announced general availability of EC2 P6-B200 instances for SageMaker notebooks. These instances include eight NVIDIA Blackwell GPUs with 1,440 GB of high-bandwidth GPU memory and 5th Gen Intel Xeon processors, offering up to 2x the training performance versus P5en. They enable interactive development and fine-tuning of large foundation models in JupyterLab and CodeEditor, and are available in US East (Ohio) and US West (Oregon).

Amazon SageMaker Adds Restricted Classification Terms

🔒 Amazon SageMaker Catalog now supports governed classification using Restricted Classification Terms, enabling catalog administrators to mark sensitive glossary terms so only authorized users or projects can apply them to assets. Administrators grant usage through explicit policies and group membership, allowing centralized governance teams to control labels like Seller-MCF or PII. The capability is available in all regions that support SageMaker Unified Studio; consult the user guide to get started.

AWS SageMaker Adds P5.4xlarge with NVIDIA H100 GPU

🚀 Amazon SageMaker Training and Processing Jobs now supports the new EC2 P5 instance size with a single NVIDIA H100 GPU, offering the P5.4xlarge configuration for cost‑effective ML and HPC workloads. The instance enables fine-grained scaling so customers can begin with smaller configurations and expand incrementally, improving cost management and infrastructure flexibility. P5.4xlarge is available via SageMaker Flexible Training Plans and in select regions through On‑Demand and Spot.