All news with #amazon sagemaker ai tag

🔐 SageMaker MLflow now supports customer-managed keys (CMK) via AWS Key Management Service (KMS). This enhancement lets organizations with strict security or compliance needs manage encryption keys themselves and gain enhanced control and auditing through AWS CloudTrail. Customer-managed keys must be symmetric and created in the same AWS account and region as the MLflow App. The feature is generally available in all Regions where MLflow App is offered.

🛠️ Amazon SageMaker HyperPod now integrates Ray with built-in observability, resilient distributed training, accelerated inference, and managed development environments. Data scientists can create and manage Ray clusters from SageMaker Studio, attach JupyterLab or a local IDE for interactive iteration, and use Grafana and Amazon Managed Service for Prometheus for one-click observability. HyperPod provides node auto-recovery, hung job detection, tiered checkpointing, and task governance to improve GPU utilization and reliability, plus a tiered KV cache and JumpStart model deployment for faster Ray Serve inference.

🚀 SageMaker AI Studio now offers Generative AI Inference Recommendations, providing a guided low-code/no-code workflow to identify optimal inference configurations for generative workloads. The feature builds on an April 2026 API launch and benchmarks candidate setups on real GPU infrastructure using NVIDIA AIPerf, applying techniques like speculative decoding and kernel tuning. Users pick a use-case profile, optimization goal, and model source, then receive ranked, production-ready recommendations that can be deployed directly to SageMaker endpoints, with only standard compute costs for benchmarking.

🧭 Amazon SageMaker Notebooks now support Trusted Identity Propagation (TIP) with Amazon Athena, Amazon Redshift, and Amazon EMR Serverless, enabling per-user access control for data analytics. When connected to a TIP-enabled compute in a TIP-enabled Project, each notebook user's IAM Identity Center identity flows through to AWS Lake Formation, ensuring they see only the tables, columns, and rows their permissions allow. TIP provides per-user data boundaries, full audit attribution with CloudTrail, and reduces admin friction by automatically propagating identity through existing compute connections without extra logins or role management. The feature is available in all Regions where Amazon SageMaker Unified Studio is available.

🔍 Amazon SageMaker Unified Studio now integrates data profiling and anomaly detection powered by AWS Glue Data Quality. Data stewards, engineers, and analysts can generate dataset- and column-level statistics on catalog tables and Visual ETL job results to understand data shape and completeness. A dedicated Data profile tab supports on-demand and scheduled profiling while anomaly detection flags drift without predefined thresholds. These capabilities are available in all Regions where SageMaker Unified Studio is offered.

🔔 Amazon announced general availability of Amazon Nova Multimodal Embeddings in AWS GovCloud (US-West). The unified embedding model supports text, documents, images, video, and audio through a single model to enable cross-modal retrieval and agentic RAG. It accepts up to 8K tokens and video/audio segments up to 30 seconds, with synchronous and asynchronous API options for latency-sensitive and bulk workloads.

🔍 LocateAnything-3B, Qwen-AgentWorld-35B-A3B, and Qwen3.5-122B-A10B are now available on Amazon SageMaker JumpStart. These models provide visual grounding, agent environment simulation, and large-scale multimodal reasoning capabilities. Customers can deploy them with a few clicks via the SageMaker console or programmatically using the SageMaker Python SDK. The models expand foundation model choices for enterprise AI on AWS.

🚀 NVIDIA Nemotron 3.5 Lightning is now available on Amazon SageMaker JumpStart, enabling customers to deploy a high-throughput open model optimized for persistent agent workloads. The 30B-parameter hybrid MoE design activates 3B parameters per pass, delivering up to 4x throughput (~410 tokens/sec) and 30% faster task completion. It supports up to 1M-token context and can be post-trained and deployed across edge, on-premises, or cloud environments.

🧭 AWS Glue now lets users open Amazon SageMaker Unified Studio with a single click from the Glue console, enabling analysts and engineers to query cataloged data, run data quality checks, and build pipelines without switching contexts. Access from S3 Tables, Athena, EMR, Redshift and Glue consoles is supported, and the same IAM role is used when launching SageMaker Notebooks. An inline permissions panel simplifies setup by letting you create and customize required IAM policies in-context if SageMaker Unified Studio isn't yet configured.

🔔 Amazon SageMaker JumpStart now includes Black Forest Labs' FLUX.2-small-decoder and Google's gemma-4-12B-it, expanding foundation model options for AWS customers. FLUX.2-small-decoder offers faster image decoding with lower VRAM use, while gemma-4-12B-it provides unified multimodal understanding across text, image, and audio. Customers can deploy these models via the SageMaker console or the SageMaker Python SDK for scalable AI solutions.

🔍 Redis's langcache-embed-v3-small, JetBrains' Mellum2-12B-A2.5B-Thinking, and LightOn's LightOnOCR-2-1B are now available on Amazon SageMaker JumpStart. These models support semantic caching, code-focused reasoning, and end-to-end document OCR respectively, enabling scalable deployment on AWS. Customers can deploy them via the SageMaker JumpStart catalog or the SageMaker Python SDK with minimal effort.

🆕 Amazon SageMaker JumpStart now offers three foundation models: Z.ai’s GLM-5.2 FP8, NVIDIA’s Nemotron-Nano-12B-v2, and Z.ai’s GLM-OCR. These models cover long-horizon agentic engineering, efficient hybrid reasoning, and advanced document understanding, enabling customers to deploy high-performance AI on AWS with minimal setup. Each model is optimized for specific enterprise workflows and can be deployed via the SageMaker console or SDK.

🚀 Amazon EMR on EC2 now supports interactive Apache Spark sessions via Spark Connect, enabling data engineers and scientists to develop and debug Spark applications from managed notebooks in Amazon SageMaker Unified Studio or from IDEs like Jupyter and VS Code. Each interactive session runs on a dedicated EMR on EC2 cluster, and users can monitor and debug active and completed sessions through the EMR console. The feature offers persistent Spark contexts across cells, a client-server architecture that decouples clients from the Spark driver, and real-time observability through the Spark UI and History Server.

🚀 Amazon SageMaker Studio notebooks now support EC2 G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 offers up to 4.6x AI inference performance versus G6, with up to 8 GPUs and 700 Gbps EFA-enabled bandwidth to accelerate inference, graphics, and analytics workloads. These instances are available in AWS US East (N. Virginia and Ohio) and US West (Oregon). Refer to developer guides for JupyterLab and CodeEditor setup and the pricing page for cost details.

🔥 Amazon SageMaker now supports serverless full fine-tuning for more than 25 open-source models, including families such as gpt-oss, Gemma, Llama, Nemotron, and Qwen. In addition to parameter-efficient methods like LoRA, SageMaker customers can update all model parameters to achieve deeper domain adaptation when needed. The service manages infrastructure provisioning and training orchestration, and billing is usage-based.

🔗 Amazon SageMaker Unified Studio now supports Teradata Vantage as a data source, enabling users to query and analyze enterprise data warehouse assets alongside other data in a single governed environment. This integration lets you combine Teradata data with sources such as Amazon Redshift, Amazon S3, and relational databases to correlate analytical and operational workloads. Data engineers and analysts can query Teradata data in the data explorer, use the query editor and notebooks, or include it in visual ETL jobs without leaving the studio.

🔧 Amazon SageMaker Unified Studio now provides full Git version control directly within core tools like Query Editor, Visual ETL, Workflows, and Notebooks. The updated Repositories experience replaces automatic sync with flexible, file-level control and lets teams choose which files to track in Git on GitHub, GitLab, or Bitbucket. Repositories are decoupled from project creation, support multiple repos and branches concurrently, and preserve CLI access via JupyterLab or Code Editor terminals.

🔎 Amazon SageMaker Unified Studio now supports Amazon OpenSearch as a data source, allowing you to query and analyze search and log analytics data alongside other assets. You can combine OpenSearch data with sources like Amazon Redshift, Amazon S3, and relational databases within a single governed environment. The integration enables correlating operational search data with analytical datasets for cross-source insights and streamlined workflows.

📈 Amazon Managed Service for Prometheus now supports up to 1.5 billion active metric time series and up to 200,000 recording and alerting rules per workspace, with customers able to create many workspaces per account. Amazon Managed Service for Prometheus is a fully managed, Prometheus-compatible monitoring service that automatically scales ingestion and storage for high-cardinality workloads across containerized, serverless, and hybrid environments. It integrates with AWS security services to provide secure access to monitoring data and lets customers request higher workspace limits via AWS Support Center or AWS Service Quotas.

🛠️ Amazon RDS for SQL Server now supports Microsoft SQL Server 2025 across Enterprise, Standard, and Developer editions. SQL Server 2025 integrates AI into the database engine, enabling T-SQL to call external REST endpoints and work with AWS services like Amazon Bedrock, Amazon SageMaker, Amazon S3, and AWS Lambda. Standard Edition capacity and features have been expanded, and a new free Developer edition is available for testing.