All news with #amazon sagemaker ai tag

🔒 Amazon SageMaker Unified Studio now supports domain management for both Identity Center and IAM-based domains outside the AWS Console. Administrators and data management teams can create and manage projects, configure workforce identity, administer users and permissions, and set networking properties. VPC configuration and account associations are consistent across domain types and available in all Regions where Unified Studio is offered.

🛠️ Amazon SageMaker Unified Studio now adds business context, metadata, and data governance features for IAM-based domains. Customers can annotate AWS Glue Data Catalog tables with business names, descriptions, and README documentation, and use AI-generated metadata to automate cataloging. Teams can build business glossaries, define metadata form templates, and capture structured attributes like classification, retention, and ownership. These capabilities enable search, filtering by glossary or metadata fields, and access requests with automated Lake Formation permission grants, and are available in all regions where SageMaker Unified Studio is supported.

🔧 Amazon SageMaker Unified Studio now automatically creates Glue connections across subnets to enable job retries when a primary subnet becomes unavailable. Administrators define a domain VPC with multiple private subnets and the system provisions connectors for new projects so retries can run on alternate subnets without manual intervention. This reduces unplanned data-pipeline downtime and helps meet SLAs across AWS Regions where SageMaker Unified Studio is available.

🧩 Amazon SageMaker Inference now supports OpenAI-compatible APIs, enabling existing tools and frameworks like the OpenAI SDK, LangChain, and Strands Agents to connect directly to SageMaker endpoints. Switching requires only changing an endpoint URL, with no custom integration code or SDK wrappers. You can continue using your current authentication approach while choosing GPU instances, keeping data in your VPC, running open source or fine-tuned models, and leveraging auto-scaling policies. This capability is available today across multiple AWS regions with AWS credentials and automatic token refresh for production use.

🛠️ Amazon SageMaker Unified Studio now integrates data quality rule authoring and evaluation powered by AWS Glue Data Quality. Data engineers, analysts, and data scientists can define rules, run evaluations, and view results for both data at rest and data in transit. The feature supports catalog table checks and Visual ETL job evaluations to detect issues before they impact analytics or ML workloads.

🧾 Amazon SageMaker HyperPod now supports data capture for inference workloads, allowing organizations to record request and response payloads for monitoring, compliance, debugging, and offline analysis. You can capture traffic at the SageMaker endpoint, load balancer, or model pod and combine layers for richer observability. Captured data is delivered asynchronously to Amazon S3 with configurable sampling and encryption using customer-managed AWS KMS keys and is designed to never block inference. Enable data capture via the HyperPod Inference Operator or SageMaker JumpStart.

🚀 Amazon SageMaker Studio IDEs, including JupyterLab and Code Editor, now support GPU capacity reservations via SageMaker Flexible Training Plans (FTP), offering predictable access to high-performance resources and up to 65% cost savings versus On‑Demand. FTP provides a self-serve procurement flow to select instance type, reservation length, and start date. Studio apps can be launched using the purchased plan from the Instance dropdown, with automatic provisioning and proactive expiration notifications to protect work.

🚀 Amazon SageMaker AI now supports serverless customization for the Qwen3.6 27B parameter model using supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). This extends SageMaker's existing fine-tuning support for Qwen3.5 and other open-weight models. Serverless customization removes infrastructure management—SageMaker handles provisioning and orchestration—so teams pay only for what they use. The feature is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and EU (Ireland).

🔔 AWS has added GLM-5.1-FP8 (from Z.ai) and Phi-4-mini-instruct (from Microsoft) to Amazon SageMaker JumpStart, expanding foundation model choices for enterprise workloads. GLM-5.1-FP8 targets agentic software engineering and multi-round optimization for repository-level code, debugging, and long-horizon automation. Phi-4-mini-instruct provides compact, low-latency reasoning across 24 languages and supports function calling for edge and latency-sensitive use cases. Customers can deploy these models via SageMaker Studio or the SageMaker Python SDK in a few clicks.

🔊 AWS has added three Qwen speech foundation models—Qwen3-TTS-12Hz-1.7B-CustomVoice, Qwen3-TTS-12Hz-1.7B-Base, and Qwen3-ASR-1.7B—to Amazon SageMaker JumpStart. The models deliver multilingual text-to-speech and automatic speech recognition capabilities across more than 10 languages and 52 languages/dialects. CustomVoice offers instruction-driven control over timbre, emotion, and prosody while Base enables 3-second rapid voice cloning. SageMaker JumpStart lets customers deploy these models from SageMaker Studio or via the SageMaker Python SDK with a few clicks.

🆕 AWS added FLUX.2-klein-base-4B and Qwen3-Embedding-0.6B to Amazon SageMaker JumpStart. FLUX.2 targets real-time image generation and multi-reference editing in a compact architecture that can run on consumer GPUs with about 13GB VRAM. Qwen3-Embedding delivers instruction-aware, multilingual text embeddings across 100+ languages for retrieval, RAG, and semantic search. Customers can deploy these models via SageMaker Studio or the SageMaker Python SDK.

🧭 Amazon SageMaker Data Agent is now available in SageMaker Unified Studio domains configured with IAM Identity Center. The agent enables data analysts and engineers to describe analysis goals in plain English and receive working Python or SQL code for connected sources such as Amazon Athena, Amazon Redshift, Amazon S3, and AWS Glue Data Catalog. It preserves conversational context across notebook cells, selected tables, and query history, proposes step-by-step plans, and includes a Fix with AI feature to help debug execution errors. The capability is available in all commercial AWS Regions where Unified Studio is supported.

🔒 Amazon SageMaker Feature Store now supports the SageMaker Python SDK v3, providing modular APIs to manage feature groups with less boilerplate. Data scientists can enable Lake Formation access controls to enforce column- and row-level permissions on offline store data at feature group creation. The SDK also exposes Apache Iceberg table properties for configuring compaction and snapshot expiration to optimize storage and queries. Available in all AWS Regions where Feature Store is offered; install v3.8.0 or later to begin.

🚀 Amazon announces general availability of EC2 P6-B200 instances in AWS US East (N. Virginia) for use with SageMaker Studio notebooks. These instances feature eight NVIDIA Blackwell GPUs, 1440 GB of high-bandwidth GPU memory, and 5th Generation Intel Xeon (Emerald Rapids) processors, offering up to 2x training performance vs P5en. They enable interactive development and fine-tuning of large foundation models directly in JupyterLab or CodeEditor for generative AI workloads.

🚀 Amazon Web Services has announced general availability of Amazon EC2 P5.4xl instances for SageMaker Studio notebooks, powered by NVIDIA H100 Tensor Core GPUs. These instances offer up to 4x faster time-to-solution versus previous-generation GPU instances and claim up to 40% lower training cost for ML models. They are designed to accelerate training and deployment of demanding DL and HPC workloads, including large language models and diffusion models. P5.4xl is available now in select US, Asia Pacific, and South America regions, with developer guides and pricing details provided.

🚀 Amazon Web Services announced general availability of Amazon EC2 G6 instances for SageMaker Studio notebooks in the Middle East (Dubai) and Asia Pacific (Malaysia). G6 instances pair up to eight NVIDIA L4 Tensor Core GPUs (24 GB each) with third-generation AMD EPYC processors, delivering roughly 2× better deep-learning inference performance than G4dn. These instances support interactive model deployment and training for generative AI fine-tuning, NLP, vision, and recommender workloads. Refer to developer guides for JupyterLab and CodeEditor setup and the pricing page for cost details.

🚀 Amazon Web Services announced general availability of EC2 G6e instances on SageMaker Studio notebooks in Dubai, Tokyo, Seoul, Frankfurt, Stockholm and Spain. G6e instances provide up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB per GPU and 3rd‑generation AMD EPYC processors, delivering up to 2.5× performance versus G5. They target interactive model testing, training and generative AI fine‑tuning, and can host LLMs up to 13B parameters as well as diffusion models for image, video and audio generation. Developer guides cover JupyterLab and CodeEditor setup; pricing is available on the AWS pricing page.

🚀 Amazon Web Services has announced the general availability of EC2 P4de instances on SageMaker Studio notebooks in Asia Pacific (Tokyo, Singapore) and Europe (Frankfurt). Each P4de packs eight NVIDIA A100 GPUs with 80GB HBM2e (640GB total), offering 2× the per‑GPU memory versus P4d. AWS reports up to 60% faster ML training and roughly 20% lower training cost compared to P4d, benefiting large high‑resolution datasets and reducing model training time. Developers can follow the SageMaker JupyterLab and CodeEditor guides and consult pricing for cost planning.

🧭 Amazon SageMaker Unified Studio introduces a getting-started section with short tutorials that guide users through core workflows—running a first SQL query, analyzing notebook data, building a Visual ETL pipeline, and training an ML model—each using pre-loaded sample data and completable in under 10 minutes. The development environment now auto-matches your OS light/dark mode on first sign-in. A new in-product “What’s New” area surfaces release notes and recent feature announcements to help users discover capabilities as they launch.

🔐 Amazon announced new administration features for SageMaker Unified Studio that give administrators finer control over identity configuration and user management across both IAM and IAM Identity Center domain types. Administrators can now configure AWS IAM Identity Center for SSO onboarding, add IAM roles, users, and groups as project members, and manage domain users from a consolidated admin portal. For Identity Center domains, federated access through IAM roles now produces unique user sessions so collaborators sharing a role do not overwrite each other and actions remain auditable. These updates enable teams to use corporate IAM or IAM Identity Center identities consistently across domains and simplify collaboration and auditing in the Studio environment.