All news with #amazon sagemaker ai tag

⏳ Amazon SageMaker Unified Studio now supports long-running background sessions using corporate identities via AWS IAM Identity Center's trusted identity propagation (TIP). Users can launch interactive notebooks and data processing on SageMaker, Amazon EMR, and AWS Glue that persist when they log off or experience network or credential interruptions. Sessions retain corporate permissions and can run up to 90 days (default 7 days), reducing the need for continuous monitoring and improving productivity for multi-hour or multi-day workloads.

🔒 Amazon SageMaker Unified Studio now supports EMR on EKS as a compute option for interactive Apache Spark sessions, bringing containerized, large-scale distributed compute with automatic scaling and cost optimizations directly into the Studio environment. The feature adds trusted identity propagation through AWS Identity Center, enabling single sign-on and end-to-end data access traceability for interactive analytics. Data practitioners can use corporate credentials to access Glue Data Catalog resources from SageMaker JupyterLab while administrators retain fine-grained access controls and audit trails. This capability is available in all existing SageMaker Unified Studio regions.

📣 Amazon SageMaker Catalog now supports custom column-level metadata forms and markdown-enabled rich text descriptions so data stewards can attach business-specific key-value metadata and formatted documentation directly to individual columns. Form values and rich text are indexed in real time and become immediately searchable alongside column names, descriptions, and glossary terms. This capability is available in all AWS Regions where SageMaker is supported.

📌 Amazon SageMaker Catalog now enforces glossary-term metadata during asset publishing. Administrators can require data producers to tag assets with approved business vocabulary from organizational glossaries, and enforcement rules will block publication if required terms are missing. This standardizes metadata, aligns technical schemas with business language, and improves discoverability and governance. Available in all regions where Amazon SageMaker Catalog operates; policies can be managed via the console, CLI, or SDKs.

📂 Amazon SageMaker Catalog now supports read and write access to Amazon S3 general purpose buckets, enabling data scientists and analysts to discover, process, and share unstructured data alongside structured datasets. Data publishers can grant read-only or read/write permissions when approving subscriptions or sharing S3 data, allowing processed outputs to be written back to the original bucket or folder. This feature is available in all Regions that support SageMaker Unified Studio and can be accessed via the studio UI, the Amazon DataZone API, SDK, or AWS CLI.

🔔 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.

🔖 Amazon SageMaker Unified Studio now lets administrators define custom tags that are applied to resources created by a SageMaker project. Administrators configure project profiles to supply tag key/value pairs or keys with default values that users can modify during project creation, helping enforce tagging standards and support SCPs and cost allocation. This initial release is API-only and available across all supported AWS Regions.

📊 Data engineers and analysts can now connect Amazon SageMaker Unified Studio to existing Amazon Athena workgroups to run SQL queries using the workgroups' default settings and properties. This lets teams reuse access controls, cost limits, and query-tracking policies already defined in Athena, reducing setup time while maintaining governance. To enable it, choose 'Add compute' → 'Connect to existing compute resources' in Unified Studio; the connected Athena workgroup then appears in the query editor and is available in all regions where Unified Studio is supported.

🔁 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.

🔍 AWS has integrated Amazon Q's AI diagnostics into the AWS Step Functions console to provide context-aware troubleshooting for workflow errors. Users can click the Diagnose with Amazon Q button in error alerts and the console notification area to receive tailored remediation steps for state machine execution failures and Amazon States Language (ASL) syntax errors and warnings. Troubleshooting recommendations appear in a dedicated window showing remediation steps, analysis of relevant state, input, and logs, and suggested fixes to reduce manual investigation. The feature is automatically enabled in commercial AWS Regions where Amazon Q is available to help teams accelerate resolution and lower operational overhead.

🛠️ 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.

🧠 You can now run asynchronous batch AI inference inside Amazon OpenSearch Ingestion pipelines to enrich and ingest very large datasets for Amazon OpenSearch Service domains. The same AI connectors previously used for real-time calls to Amazon Bedrock, Amazon SageMaker, and third parties now support high-throughput, offline jobs. Batch inference is intended for offline enrichment scenarios—generating up to billions of vector embeddings—with improved performance and cost efficiency versus streaming inference. The feature is available in regions that support OpenSearch Ingestion on domains running 2.17+.

🛡️ Amazon SageMaker managed MLflow is now available in both AWS GovCloud (US-West) and AWS GovCloud (US-East) regions. The managed service integrates MLflow experiment tracking with SageMaker capabilities, streamlining AI experimentation and accelerating GenAI development from idea to production. It provides end-to-end observability to help reduce time-to-market and simplify compliance and operational oversight for government workloads.

🛠️ 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 announces general availability of the SageMaker HyperPod health monitoring agent for Slurm clusters. The agent runs continuously on GPU- and Trainium-based nodes to perform passive background checks, detect hardware faults (for example, unresponsive GPUs and NVLink errors), and mark and replace unhealthy nodes automatically. It supports automatic reboots and coordinates with Slurm job auto-resume so training can continue from the last checkpoint, reducing manual intervention and downtime.

🚀 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).

🔗 AWS now enables remote connections from local VS Code to Amazon SageMaker Unified Studio, allowing developers to use their personalized VS Code setups while running workloads on SageMaker-managed compute and accessing cloud-resident data. Authentication is provided via the AWS Toolkit extension for secure, streamlined access. The integration preserves existing development workflows for data processing, SQL analytics, and ML.

⚡ Amazon Web Services has announced general availability of managed tiered checkpointing for Amazon SageMaker HyperPod, a hybrid checkpointing capability that caches frequent checkpoints in CPU memory and periodically persists them to Amazon S3 for durability. The approach reduces model recovery time and minimizes training progress loss on large-scale clusters. It integrates with PyTorch Distributed Checkpoint (DCP) and is enabled via a CreateCluster/UpdateCluster API parameter; customers can use the sagemaker-checkpointing Python library to adopt it with minimal code changes. Currently available for HyperPod clusters using the EKS orchestrator.

🔧 AWS announced general availability of Custom Blueprints in Amazon SageMaker Unified Studio, enabling customers to supply their own managed IAM policies when creating project roles. Teams can replace or augment the default service-managed policies and use custom AWS CloudFormation templates to define infrastructure and parameters for resources such as Amazon EMR on EC2, AWS Glue Data Catalog, and Amazon Redshift. Sample templates are available in the SageMaker documentation, and the capability is offered in all AWS Commercial Regions where the next-generation SageMaker is available.

🤖 Amazon Web Services announced enhancements to the Amazon Q Developer chat experience within SageMaker Unified Studio Jupyter notebooks and added a command-line interface for use in notebooks and the Code Editor. By integrating with Model Context Protocol (MCP) servers, the assistant becomes aware of project resources—data, compute, and code—and provides personalized, context-aware help. These updates aim to speed tasks like code refactoring, file edits, and troubleshooting while preserving transparency around assistant actions. The capabilities are available at no additional cost via the Amazon Q Developer Free Tier where SageMaker Unified Studio is offered; customers can enable Amazon Q Developer Pro for expanded functionality.