All news with #amazon sagemaker ai tag

🔍 AWS Cost Explorer introduces Analyze with Amazon Q, enabling one-click, contextual cost explanations for any configured report. Amazon Q Developer provides analysis of cost trends, top drivers, anomalies, and optimization suggestions using your exact filters and time period. The feature adapts explanations to historical, forecast, or mixed-date views and supports follow-up questions while preserving conversation context. It is available in all commercial AWS Regions at no additional charge.

🧭 Amazon SageMaker Data Agent now integrates with SageMaker Catalog business context and metadata, letting data practitioners discover datasets and generate more accurate SQL and Python code using business terminology rather than cryptic table names. The agent leverages curated catalog content, including metadata synced from Collibra, Atlan, and Alation, to identify tables and columns, plan multi-step workflows, and respect governance by checking subscription status and providing access request links. This feature is available in SageMaker Unified Studio notebooks and the Query Editor in regions where Unified Studio is offered.

💬 Amazon SageMaker Data Agent in SageMaker Unified Studio now supports conversation history, enabling practitioners to maintain continuity across analytical sessions. Data analysts and scientists can reference prior agent-generated code, resume multi-step analyses, and review past troubleshooting interactions within notebooks and the Query Editor. Conversation history appears as a scrollable list via the clock icon, with auto-generated titles and timestamps for quick identification.

🧭 Amazon SageMaker Unified Studio now allows scheduling, parameterizing, and orchestrating notebook runs directly from the notebook interface without external orchestration infrastructure. Users can run notebooks in the background on dedicated compute, create recurring schedules, and reuse parameterized notebooks across different inputs. The Notebook Operator enables chaining multi-notebook workflows, and AI-assisted troubleshooting with SageMaker Data Agent helps diagnose failures and create schedules using natural language.

🔧 Amazon SageMaker Unified Studio now offers a localized user interface in twelve languages, including Simplified and Traditional Chinese, French, German, Japanese, Korean, Spanish, Portuguese (Brazilian), Italian, Indonesian, Turkish, and American English. Language selection is automatic via the browser or manually set through the profile Language selector, and the choice applies across the entire studio. This localization is available in all AWS Regions where SageMaker Unified Studio is offered and supports both AWS IAM Identity Center-based and IAM-based domains.

🚀 Amazon ECS Managed Instances now supports AWS Trainium and AWS Inferentia accelerators, enabling scalable training and inference for generative AI workloads. This fully managed compute option offloads infrastructure operations to AWS while preserving the full capabilities of Amazon EC2. You can select Inferentia2, Trainium1, or Trainium2 when creating a capacity provider and set NEURON_CORE=all to allocate the accelerator per task. Management charges apply in addition to standard EC2 costs.

🧭 Amazon SageMaker AI introduces multi-turn reinforcement learning (RL), a serverless model customization method for fine-tuning models on multi-step, agentic tasks. The feature trains models against users' agent environments, rewarding entire decision sequences to improve task accuracy of smaller, cost‑effective models versus larger general-purpose models. It integrates with Amazon Bedrock AgentCore Runtime and other deployment targets, and handles rollout orchestration, trajectory collection, training, and checkpoints, with MLflow tracking and evaluation metrics. Multi-turn RL runs serverlessly and is available in SageMaker Studio and the SageMaker Python SDK, supporting several foundation models in specific regions.

🔧 Amazon SageMaker Studio's quick setup now completes in under twenty seconds, down from over two minutes, letting users rapidly move from sign-in to a fully configured Studio environment. Newly created Studio environments automatically receive serverless model customization permissions via a new managed policy, AmazonSageMakerModelCustomizationCoreAccess, enabling fine-tuning, evaluation, and deployment without manual IAM role configuration. Existing environments receive actionable guidance to add the permissions. The feature is available in all AWS Commercial Regions that support SageMaker Studio.

🔒 Amazon SageMaker Unified Studio now supports custom IAM permissions boundaries so organizations enforcing Service Control Policies (SCPs) can provision projects without changing their security posture. When creating a project, SageMaker provisions three IAM roles — a project user role, an Amazon Bedrock service role, and a Bedrock Lambda execution role — and administrators can specify a permissions boundary in the Tooling blueprint configuration. The boundary is attached to all three roles at creation, satisfying SCP requirements and limiting role capabilities while allowing automatic project provisioning across all supported AWS Regions.

🔧 Amazon SageMaker HyperPod now supports EFA-only network interfaces for cluster instance groups, allowing dedicated Elastic Fabric Adapter devices without attaching Elastic Network Adapters for IP networking. This reduces IP address consumption in VPC subnets and enables larger-scale distributed training clusters. To enable it, set efa-only in the ClusterNetworkInterface when creating or updating a HyperPod cluster via the API.

🔧 Amazon SageMaker HyperPod now offers troubleshooting skills that deliver expert AI/ML cluster diagnostics into coding assistants like Claude Code, Cursor, and Kiro. These skills guide natural-language diagnostics for GPU hardware faults, NCCL communication issues, and performance bottlenecks across distributed clusters. They automate evidence collection via AWS Systems Manager and provide actionable recommendations without requiring infrastructure changes.

🚀 Amazon announced the general availability of EC2 P6-B200 instances on SageMaker notebook instances in AWS US East (N. Virginia). These instances feature 8 NVIDIA Blackwell GPUs with 1440 GB GPU memory and 5th Gen Intel Xeon processors, offering up to 2x performance versus P5en for AI training. Customers can use them to develop and fine-tune large foundation models interactively in JupyterLab or CodeEditor on SageMaker Studio.

🚀 Amazon EC2 P4de instances are now generally available on SageMaker notebook instances in Asia Pacific (Tokyo). These instances feature 8 NVIDIA A100 GPUs with 80GB HBM2e each, delivering 640GB total GPU memory and up to 60% better ML training performance versus P4d. Customers can expect reduced model training times, improved handling of high-resolution datasets, and about 20% lower training cost. See developer guides for setup details on SageMaker Studio and notebook instances.

🚀 Amazon announces general availability of EC2 P5.48xl instances in Asia Pacific (Tokyo) for use with SageMaker notebook instances. These instances use NVIDIA H100 Tensor Core GPUs to accelerate deep learning and HPC workloads, promising up to 4x faster time to solution and up to 40% lower training costs versus previous GPU generations. Customers can leverage P5 instances to train and deploy complex LLMs and diffusion models for generative AI tasks. Developer guides provide setup instructions for JupyterLab and CodeEditor on SageMaker Studio and notebook instances.

🚀 Amazon SageMaker notebook instances now support EC2 P5.4xl instances powered by NVIDIA H100 Tensor Core GPUs. These instances deliver up to 4x higher performance and up to 40% lower training cost versus prior-generation GPU instances, accelerating development of deep learning and generative AI models. P5.4xl is generally available across multiple AWS regions including US East, US West, Asia Pacific, and South America. Refer to developer guides for setup instructions in JupyterLab and CodeEditor on SageMaker Studio and notebook instances.

🚀 Amazon announces GA of EC2 P5en.48xl instances for SageMaker notebook instances, delivering advanced H200 GPUs paired with 4th Gen Intel Xeon processors. These instances provide increased GPU memory and bandwidth compared to P5, Gen5 PCIe between CPU and GPU, and faster EFA/Nitro networking to boost distributed training and inference. P5en.48xl is available in US East (N. Virginia, Ohio), US West (Oregon), and Asia Pacific (Tokyo) regions. Refer to the developer guides for setup and SageMaker Studio integration.

🚀 Amazon Web Services announces general availability of Amazon EC2 P5en.48xl instances on SageMaker notebook instances. These P5en instances feature 8 H200 GPUs with increased GPU memory and bandwidth versus H100, paired with custom 4th Gen Intel Xeon processors and Gen5 PCIe for higher CPU–GPU bandwidth. They also include third-generation EFA via Nitro v5, offering up to 3200 Gbps and latency improvements over prior P5 instances. P5en.48xl is currently available in US East (N. Virginia, Ohio), US West (Oregon), and Asia Pacific (Tokyo).

🚀 Amazon SageMaker notebook instances now support EC2 P5.4xl instances powered by NVIDIA H100 GPUs. These instances boost deep learning and HPC workloads, offering up to 4x faster time-to-solution and up to 40% lower training cost versus prior GPU generations. P5.4xl is available in multiple AWS regions including US East, US West, Asia Pacific, and South America; see AWS developer guides for setup instructions.

🔔 Amazon SageMaker HyperPod now supports minimum capacity requirements (MinCount) for Slurm clusters using continuous provisioning, ensuring that instance groups only enter InService when a specified minimum number of instances are provisioned. This is valuable for distributed training frameworks such as PyTorch FSDP, Megatron-LM, and NVIDIA NeMo that need a fixed node count, and for teams requiring baseline GPU counts to meet SLAs or cost targets. Specify MinInstanceCount in the CreateCluster or UpdateCluster API; groups remain in Creating or Updating until the threshold is met or a 3-hour rollback triggers. MinCount is available in all Regions supporting SageMaker HyperPod.

🧭 Amazon SageMaker Unified Studio IAM domains now include an interactive interface for creating and managing feature groups in SageMaker Feature Store, removing the need to write code for common feature management tasks. This launch makes feature management accessible to data scientists, ML engineers, and business analysts from a single collaborative environment. Users can discover and search existing features, create and modify feature groups, view definitions and schemas, and monitor data ingestion status without API calls. Features created elsewhere appear immediately in SageMaker Unified Studio when sharing the same IAM role, enabling seamless workflows across the ML lifecycle.