All news with #amazon sagemaker ai tag

🚀 AWS announced a major expansion of its AI Competency, validating 60 partners across three new Agentic AI categories: Agentic AI Tools, Agentic AI Applications, and Agentic AI Consulting Services. The launch includes an AI agent in AWS Partner Central to provide immediate feedback and speed specialization approvals. Validated partners demonstrate production-grade capabilities using services such as Amazon Bedrock AgentCore, Strands Agents, and Amazon SageMaker AI, and must meet AWS standards for security, reliability, and responsible AI.

🤖 AWS has launched the AWS AI League 2026 Championship, expanding its flagship AI tournament with new challenge tracks and a doubled prize pool of $50,000 to drive builder innovation. The program pairs a brief orientation with two competition tracks: a Model Customization track using Amazon SageMaker AI to fine-tune foundation models for domain-specific tasks, and an Agentic AI track using Amazon Bedrock AgentCore to build planning and execution agents. Enterprises can apply to host internal tournaments and receive AWS credits to run team competitions, while individual developers can compete at AWS Summits to test skills and build with AWS AI services.

⚡ Amazon SageMaker HyperPod now offers Managed Tiered KV Cache and Intelligent Routing to optimize LLM inference for long-context prompts and multi-turn conversations. The two-tier cache combines local CPU memory (L1) with disaggregated cluster storage (L2) — with AWS-native tiered storage recommended and Redis optional — to reuse computed key-value pairs and reduce recomputation. Intelligent Routing directs requests using prefix-aware, KV-aware, or round-robin strategies, while built-in observability integrates with Amazon Managed Grafana and deployment is enabled via InferenceEndpointConfig or SageMaker JumpStart.

🛠️ Amazon SageMaker HyperPod now supports custom Kubernetes labels and taints configured at the instance group level via the CreateCluster and UpdateCluster APIs. You can specify up to 50 labels and 50 taints per instance group using the KubernetesConfig parameter. HyperPod automatically applies and preserves these settings across node creation, replacement, scaling, and patching, eliminating manual kubectl work and ensuring device plugin pods (EFA, NVIDIA) schedule correctly while allowing NoSchedule taints to protect costly GPU nodes.

🚀 Amazon SageMaker HyperPod is now generally available with new programmatic APIs that let administrators reboot or replace cluster nodes at scale. The BatchRebootClusterNodes and BatchReplaceClusterNodes APIs provide an orchestrator-agnostic way to recover unresponsive or degraded nodes for both Slurm and EKS clusters. Each API supports batch operations for up to 25 instances and complements existing orchestrator-specific workflows. The capabilities are currently available in US East (Ohio), Asia Pacific (Mumbai), and Asia Pacific (Tokyo) and are accessible via the AWS CLI, SDKs, or API calls.

⚙️ Amazon SageMaker AI's Flexible Training Plans (FTP) now support inference endpoints, allowing customers to reserve guaranteed GPU capacity for planned evaluations and production peaks. You choose instance types, compute requirements, reservation length, and start date, then reference the reservation ARN when creating an endpoint. SageMaker AI automatically provisions and runs the endpoint on the reserved capacity for the plan duration, removing much of the infrastructure scheduling overhead. FTP for inference is initially available in US East (N. Virginia), US West (Oregon), and US East (Ohio).

🔧 The Amazon SageMaker AI MCP Server now provides tools to set up and manage HyperPod clusters, allowing AI coding assistants to provision and operate clusters for distributed training, fine‑tuning, and deployment. It automates prerequisites and orchestrates clusters via Amazon EKS or Slurm with CloudFormation templates that optimize networking, storage, and compute. The server also delivers lifecycle operations — scaling, patching, diagnostics — so administrators and data scientists can manage large-scale AI/ML clusters without deep infrastructure expertise.

⚡ Amazon SageMaker AI now supports EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) speculative decoding to boost large language model inference throughput by up to 2.5x. The capability enables models to predict and validate multiple tokens in parallel rather than one at a time, preserving output quality while reducing latency. SageMaker automatically selects between EAGLE 2 and EAGLE 3 depending on model architecture and provides built‑in optimization jobs using curated or customer datasets. Optimized models can be deployed through existing SageMaker inference workflows without infrastructure changes, and the feature is available in select AWS Regions.

🎙️ Amazon SageMaker AI Inference now supports bidirectional streaming, enabling real-time speech-to-text transcription that returns partial transcripts while audio is still being captured. Using the new Bidirectional Stream API, clients open an HTTP/2 connection to the SageMaker AI runtime, which automatically creates a WebSocket to your model container so audio frames and interim transcripts flow continuously. Any container that implements a WebSocket handler per the SageMaker AI contract works out of the box, allowing real-time models such as Deepgram to run without modification. The feature eliminates weeks or months of custom streaming infrastructure work so teams can focus on model accuracy, latency tuning, and agent behavior.

🚀 Amazon SageMaker now supports Amazon Athena for Apache Spark, combining a new notebook experience with a fast serverless Spark runtime in a single workspace. Data engineers, analysts, and data scientists can query data, run Python, develop jobs, train models, and visualize results with no infrastructure to manage and second-level billing. The service runs Spark 3.5.6, is optimized for Apache Iceberg and Delta Lake, and adds debugging, real-time Spark UI monitoring, and secure Spark Connect communication. Table-level access controls are enforced through AWS Lake Formation.

✨ Amazon SageMaker now offers one-click onboarding of existing AWS datasets into Amazon SageMaker Unified Studio, letting customers begin data work in minutes while retaining their current IAM roles and permissions. The feature provisions a pre-configured serverless notebook with a built-in AI agent that supports SQL, Python, Spark, and natural language. Users can start from SageMaker, Amazon Athena, Amazon Redshift, or Amazon S3 Tables consoles and the setup imports permissions from AWS Glue Data Catalog, Lake Formation, and S3 to accelerate first use.

🤖 Amazon SageMaker Data Agent is a built-in AI agent in the new notebook experience that accelerates analytics and ML development. It translates natural-language prompts into detailed execution plans and generates SQL and Python code, while staying aware of notebook context and data catalog metadata. Available in multiple AWS regions, it speeds common tasks like data transformation, statistical analysis, and model prototyping.

⏳ Amazon SageMaker Unified Studio now supports long-running background sessions using corporate identities via AWS IAM Identity Center's trusted identity propagation (TIP). Users can launch interactive notebooks and data processing on SageMaker, Amazon EMR, and AWS Glue that persist when they log off or experience network or credential interruptions. Sessions retain corporate permissions and can run up to 90 days (default 7 days), reducing the need for continuous monitoring and improving productivity for multi-hour or multi-day workloads.

🔒 Amazon SageMaker Unified Studio now supports EMR on EKS as a compute option for interactive Apache Spark sessions, bringing containerized, large-scale distributed compute with automatic scaling and cost optimizations directly into the Studio environment. The feature adds trusted identity propagation through AWS Identity Center, enabling single sign-on and end-to-end data access traceability for interactive analytics. Data practitioners can use corporate credentials to access Glue Data Catalog resources from SageMaker JupyterLab while administrators retain fine-grained access controls and audit trails. This capability is available in all existing SageMaker Unified Studio regions.

📌 Amazon SageMaker Catalog now enforces glossary-term metadata during asset publishing. Administrators can require data producers to tag assets with approved business vocabulary from organizational glossaries, and enforcement rules will block publication if required terms are missing. This standardizes metadata, aligns technical schemas with business language, and improves discoverability and governance. Available in all regions where Amazon SageMaker Catalog operates; policies can be managed via the console, CLI, or SDKs.

📣 Amazon SageMaker Catalog now supports custom column-level metadata forms and markdown-enabled rich text descriptions so data stewards can attach business-specific key-value metadata and formatted documentation directly to individual columns. Form values and rich text are indexed in real time and become immediately searchable alongside column names, descriptions, and glossary terms. This capability is available in all AWS Regions where SageMaker is supported.

📂 Amazon SageMaker Catalog now supports read and write access to Amazon S3 general purpose buckets, enabling data scientists and analysts to discover, process, and share unstructured data alongside structured datasets. Data publishers can grant read-only or read/write permissions when approving subscriptions or sharing S3 data, allowing processed outputs to be written back to the original bucket or folder. This feature is available in all Regions that support SageMaker Unified Studio and can be accessed via the studio UI, the Amazon DataZone API, SDK, or AWS CLI.

🔔 Amazon SageMaker Unified Studio now delivers real-time notifications for data catalog activities, including new dataset publications, metadata changes, subscription requests, comments, and access approvals. Alerts are surfaced via a bell icon on the project home page and through a notification center that shows a recent list and a full, filterable tabular view by catalog, project, and event type. The feature is available in all regions where SageMaker Unified Studio is supported.

🔖 Amazon SageMaker Unified Studio now lets administrators define custom tags that are applied to resources created by a SageMaker project. Administrators configure project profiles to supply tag key/value pairs or keys with default values that users can modify during project creation, helping enforce tagging standards and support SCPs and cost allocation. This initial release is API-only and available across all supported AWS Regions.

📊 Data engineers and analysts can now connect Amazon SageMaker Unified Studio to existing Amazon Athena workgroups to run SQL queries using the workgroups' default settings and properties. This lets teams reuse access controls, cost limits, and query-tracking policies already defined in Athena, reducing setup time while maintaining governance. To enable it, choose 'Add compute' → 'Connect to existing compute resources' in Unified Studio; the connected Athena workgroup then appears in the query editor and is available in all regions where Unified Studio is supported.