All news with #amazon sagemaker ai tag

🛡️ Amazon GuardDuty introduces AI Protection to extend threat detection to AWS AI services such as Amazon Bedrock and Amazon SageMaker. The feature continuously monitors AI workloads for threats like anomalous invocations, cost harvesting attacks, and prompt injection, using CloudTrail management and data events to surface suspicious activity. Findings integrate with AWS Security Hub for centralized triage and can be enabled per account or centrally via AWS Organizations, with a 30-day trial available for GuardDuty customers.

🛠️ Amazon Managed Service for Apache Flink now includes AI Agent Skills that provide expert, up-to-date guidance for building and operating Flink applications. The skills cover common tasks like creating applications, troubleshooting, scaling, monitoring, networking configuration, and cost optimization. Customers can use these skills with existing AI coding agents such as Kiro, Claude Code, or Cursor by configuring the Agent Toolkit for AWS via the AWS CLI. The feature aims to speed development, simplify upgrades to versions like Flink 2.2, and help maintain healthy, performant streaming applications.

🔒 Amazon Web Services has added OpenAI’s privacy-filter to Amazon SageMaker JumpStart, offering a bidirectional token-classification model for PII detection and masking. The model is designed for fast, context-aware, tunable, high-throughput data sanitization workflows that can run on-premises. It detects PII spans like account numbers, addresses, emails, names, phones, URLs, dates, and secrets, labeling inputs in a single forward pass. Customers can deploy the model via the SageMaker Studio Models section or the SageMaker Python SDK.

🎙️ AWS added Voxtral-Mini-4B-Realtime-2602 to Amazon SageMaker JumpStart, a multilingual, low-latency speech-transcription model from Mistral AI. The model offers natively streaming architecture for real-time transcription across 13 languages and configurable delay/accuracy trade-offs. Customers can deploy it via the SageMaker Studio Models section or the SageMaker Python SDK for rapid integration into speech applications.

🔍 AWS added Qwen3-VL-Embedding-2B and Qwen3-Reranker-4B to Amazon SageMaker JumpStart, enabling modular search and retrieval pipelines. The embedding model creates multimodal vectors from text, images, screenshots, and video across 30+ languages, while the reranker scores query–document pairs across 100+ languages. Customers can deploy these models via SageMaker Studio or the SageMaker Python SDK with a few clicks.

📰 Amazon Web Services has added gemma-4-E2B-it, a multimodal instruction-tuned foundation model from Google DeepMind, to Amazon SageMaker JumpStart. The model supports text, image, audio inputs and text outputs with a built-in stepwise reasoning mode and broad capabilities including image and video understanding, OCR, function calling, and multilingual code assistance. Customers can deploy it from the SageMaker Studio Models section or via the SageMaker Python SDK for rapid integration into AWS AI workflows.

🔒 Amazon SageMaker HyperPod now supports custom AMIs for Slurm-orchestrated clusters, enabling deployment of pre-configured, security-hardened environments tailored to organizational needs. This reduces reliance on complex lifecycle scripts and improves startup times and consistency across nodes. Custom AMIs must be built from HyperPod's public base AMIs for compatibility and can be specified via the CreateCluster, UpdateCluster, or UpdateClusterSoftware APIs. The feature is available in all Regions where HyperPod is supported.

🛠️ Amazon EMR on EKS now integrates an Apache Spark troubleshooting agent that provides automated root cause analysis and PySpark recommendations through natural language, simplifying diagnosis of job failures. The agent inspects Spark History Server data, executor logs, and cluster configs to detect issues like memory errors, data skew, resource contention, and connectivity problems. Accessible via a "Troubleshoot with AI" option in the EMR on EKS console and via MCP with compatible AI coding agents, the feature is read-only, IAM-authenticated, logged in CloudTrail, and available in Regions with SageMaker Unified Studio.

🚀 Amazon SageMaker HyperPod now supports AMI-based configuration for Slurm clusters that use continuous provisioning, enabling nodes to be provisioned with required software and settings without managing lifecycle scripts in S3. AMI-based configuration installs components like Docker, Enroot, and Pyxis and applies Slurm accounting, SSH key management, and log rotation as nodes are added. To enable it, omit the LifeCycleConfig block via the API or select "None" under Lifecycle scripts in Custom setup in the console; an optional extension script can be provided via OnInitComplete and SourceS3Uri. This feature is available in all AWS Regions where SageMaker HyperPod is offered, and custom lifecycle scripts remain supported for advanced use cases.

🛠️ Amazon SageMaker Unified Studio now supports custom asset types for IAM-based domains, allowing administrators to catalog diverse asset formats such as medical images, PowerBI dashboards, or PDF reports. Administrators define a type with name, description, and optional metadata forms; assets created from those types can include glossary terms and README documentation. Published assets are discoverable and subscribable through the same governed workflow used for other catalog items and are available in all Regions where Unified Studio is offered.

🔍 Amazon SageMaker HyperPod now supports deep health checks for Slurm clusters created with continuous provisioning, enabling proactive verification of GPU accelerator health on running instances. Continuous provisioning allows asynchronous scaling of instance groups without all-or-nothing failures, and deep health checks validate hardware as nodes come online. Results and progress are visible via the SageMaker console and APIs, and failing instances are isolated and recovered automatically.

🚀 Amazon SageMaker Feature Store now supports high-throughput ingestion and record discovery with new APIs and offline-store naming options. Data scientists can use BatchWriteRecord to write multiple records across feature groups in one request, and ListRecords to page through record identifiers without prior knowledge. Offline-store configuration now allows creating Glue and Iceberg tables with custom names, simplifying cataloging and management.

🛡️ AWS Config has expanded its set of managed rules with 191 additional checks covering services such as Amazon Bedrock, SageMaker, ECS, EKS, RDS, Redshift, S3, and CloudTrail. The new rules evaluate encryption, logging, public access, network security, data protection, and operational best practices. You can deploy rules individually or as part of a conformance pack in supported AWS Regions.

📊 Amazon SageMaker Unified Studio now supports OpenLineage-compatible data lineage in IAM-based domains, capturing events from Apache Spark on Amazon EMR, AWS Glue, SageMaker Visual ETL, and notebooks. The interactive lineage graph shows data flow with configurable depth, timestamp modes for column-level detail, and a dataset-only view. You can programmatically publish, query, manage, and delete lineage events via OpenLineage APIs and the DeleteLineageEvent API.

🚀 Amazon SageMaker Unified Studio can now connect to existing Amazon Managed Workflows for Apache Airflow (MWAA) environments, enabling teams to manage Airflow workflows from within their Studio projects. To add a connection, open the Workflows tool, choose "Add connection," and supply the Airflow configuration referencing your domain and project. Once connected, project members can sync, trigger, and monitor workflows; environments running Apache Airflow 3+ also gain the visual drag-and-drop authoring experience.

🚀 Amazon SageMaker HyperPod now supports Disaggregated Prefill and Decode (DPD), which splits LLM inference into separate prefill and decode GPU pools and transfers KV cache over EFA using GPU-Direct RDMA. This reduces contention between long-context prefill and per-token decode, enabling more consistent per-token latency, higher goodput under strict latency SLOs, and independent scaling of prefill and decode. DPD is enabled via a pdSpec in the existing InferenceEndpointConfig and works with HyperPod's KV cache offloading and intelligent routing on EFA-capable instances.

🔧 Amazon SageMaker Studio now offers direct, one‑click integration with Hugging Face so users can open a fully configured Studio environment with a selected model preloaded. Previously, users had to navigate console menus, configure IAM and serverless settings, and request GPU quotas; the new flow automates environment creation and permission configuration. New customers receive a Studio environment on sign‑up; verified customers gain default GPU access and visible quota information within Studio.

🔧 Amazon SageMaker Unified Studio now supports Terraform provisioning via the open-source terraform-aws-sagemaker-unified-studio module. Platform teams can deploy domains through version-controlled templates and integrate SageMaker Unified Studio into existing infrastructure-as-code pipelines. The module manages domain infrastructure and IAM roles, includes sub-modules for blueprints and projects, and uses the Terraform AWS Cloud Control Provider.

🛠️ Amazon SageMaker HyperPod now reports AMI versions across clusters and can automatically apply backward-compatible security patches without disrupting workloads. Administrators can view AMI semantic versions (major.minor.patch), detect drift, and roll back to prior versions — preserving NVIDIA drivers, CUDA, and other bundled software — via the UpdateClusterSoftware API. Auto-patching is opt-in per instance group, applies only when nodes are idle, and avoids major/minor upgrades; it can be enabled through CreateCluster or UpdateCluster APIs. A new AMI support policy defines patch support timelines; both features are available for EKS-orchestrated HyperPod clusters in supported Regions.

🛡️ Amazon Managed Service for Prometheus is now authorized for FedRAMP High and DoD CC SRG IL-4 and IL-5 in the AWS GovCloud (US) Regions. The fully managed, Prometheus-compatible monitoring service supports high-cardinality workloads with automatic scaling of ingestion and storage. It integrates with AWS security services to provide secure access to telemetry and alerting for regulated federal and public sector environments. Customers with FedRAMP High or DoD IL-4/5 requirements can adopt the service with confidence.