All news with #amazon sagemaker ai tag

🚀 Amazon SageMaker Inference now supports container image caching to reduce scale-out latency for generative AI models. The service pre-caches the specified container image so new instances can begin serving without waiting to pull large images from Amazon ECR. This feature works with accelerator instance types, single-model endpoints, and inference component-based endpoints and requires no customer changes.

🛡️ AWS Security Hub CSPM introduces the AI Security Best Practices standard, offering 31 automated controls to detect misaligned AI resources. The standard evaluates Amazon Bedrock, Bedrock AgentCore, and Amazon SageMaker workloads against recommended configurations without manual rule creation. It covers domains like network isolation, encryption, VPC placement, KMS usage, private registries, and authorization, producing findings to help teams remediate issues. Available in all Regions where Security Hub CSPM operates, including GovCloud (US) and China.

🧰 Amazon SageMaker AI now supports serverless model customization for Gemma 4 E4B and 31B models using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT). You can adapt these Google DeepMind-built Gemma models to specific domains and workflows, and SageMaker AI handles infrastructure provisioning and training orchestration so teams pay only for what they use. The launch expands serverless customization to include models from Nova, Nemotron 3, Qwen, Llama, gpt-oss, and DeepSeek families, and is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland).

🔔 AWS is announcing availability changes affecting several services and features, with some moving to Maintenance and others entering Sunset or already at End of Support. Services moving to maintenance will not be accessible to new customers after specified dates, but existing customers can continue use. Several SageMaker features and select media and communication components are impacted. AWS provides migration guides and support resources to help affected customers.

🔍 Amazon Web Services announced the availability of all-MiniLM-L12-v2 in SageMaker JumpStart, expanding model options for customers. The Sentence Transformers model encodes sentences and short paragraphs into 384-dimensional dense vectors, enabling semantic search, clustering, and similarity tasks. Its compact architecture offers fast inference and strong embedding quality, suitable for production-scale text representation workloads.

🧭 AWS now offers Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, adding a compact multimodal foundation model optimized for edge deployment. The 14B-parameter model supports image analysis, agentic workflows with native function calling and JSON output, and multilingual understanding across dozens of languages. Customers can deploy the model from SageMaker Studio or via the SageMaker Python SDK with a few clicks to build AI assistants, agentic systems, and vision-enabled applications on AWS.

🔍 Amazon SageMaker AI now provides built-in observability for production generative AI inference, delivering real-time visibility into token performance, GPU health, inference component placement, and autoscaling behavior. The new SageMaker AI Insights dashboard in Amazon CloudWatch surfaces metrics like Time to First Token, inter-token latency, queue depth, and tokens per second alongside infrastructure health, with OpenTelemetry native metrics published automatically. Customers using Grafana can connect via a regional PromQL endpoint and import a pre-configured dashboard template for unified monitoring and faster diagnosis.

🧭 AWS Glue Interactive Sessions now supports Apache Spark Connect, enabling development and execution of Spark applications from managed notebooks like Amazon SageMaker Unified Studio or IDEs such as Jupyter and VS Code while running on AWS Glue's serverless infrastructure. The thin client architecture decouples client dependencies from the server-side Spark runtime, enabling ad hoc exploration, iterative debugging, and incremental PySpark development. Observability includes real-time Spark UI monitoring, History Server tracking, and session management via the AWS Glue API, CLI, or SDK. This capability is available across multiple AWS regions.

🆕 Amazon S3 now supports annotations, enabling attachment of custom JSON, XML, or YAML metadata to objects at up to 1GB per object to provide business context for AI agents and analytics tools. Annotations persist with objects through replication and copy operations, can be modified or deleted at any time, and share the same durability and consistency as the object. You can surface annotations in S3 Metadata for query via Apache Iceberg tables, or search them with natural language using Amazon SageMaker Unified Studio and supporting tools.

🚀 Amazon SageMaker AI now supports serverless customization for Nvidia Nemotron 3 Nano via supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). This open-weight 30B-parameter model can be deployed and adapted to specific domains and workflows directly within SageMaker. Serverless customization handles infrastructure and training orchestration, enabling teams to focus on data and evaluation while paying only for usage. The feature is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland), and can be launched from SageMaker Studio or via the SageMaker Python SDK.

🚀 Amazon SageMaker Unified Studio Notebooks now support Amazon EMR Serverless with Apache Spark Connect, enabling PySpark and Spark SQL execution on EMR Serverless Spark Applications directly from notebook cells. Users can choose their Spark runtime from the Notebook side panel, with the selection applying to both Python and SQL cells. The feature integrates with SageMaker Data Agent to generate code and execution plans from natural language prompts and offers pre-initialized capacity, unified Spark UI monitoring, and VPC connectivity for isolated workloads. It is available in all regions where SageMaker Unified Studio is offered and supports both Unified Studio notebooks and JupyterLab IDEs.

🔍 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.