All news with #amazon sagemaker ai tag

⚙️ 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 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 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 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 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 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 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.

🔁 Amazon Aurora PostgreSQL-Compatible Edition now offers zero-ETL integration with Amazon SageMaker, enabling near-real-time replication of PostgreSQL tables into a lakehouse. The synced data conforms to Apache Iceberg open standards and is immediately accessible to SQL, Apache Spark, BI, and ML tools via a simple no-code interface without impacting production workloads. Comprehensive, fine-grained access controls are enforced across analytics engines, and the capability is available in multiple AWS Regions.

🔍 AWS has integrated Amazon Q's AI diagnostics into the AWS Step Functions console to provide context-aware troubleshooting for workflow errors. Users can click the Diagnose with Amazon Q button in error alerts and the console notification area to receive tailored remediation steps for state machine execution failures and Amazon States Language (ASL) syntax errors and warnings. Troubleshooting recommendations appear in a dedicated window showing remediation steps, analysis of relevant state, input, and logs, and suggested fixes to reduce manual investigation. The feature is automatically enabled in commercial AWS Regions where Amazon Q is available to help teams accelerate resolution and lower operational overhead.

🛠️ Amazon Web Services announced that SageMaker AI Projects can now provision custom ML project templates stored in Amazon S3. Administrators can define and manage standardized end-to-end project templates in SageMaker AI Studio so data scientists can create projects that follow organizational patterns and automated workflows. The feature is available in all AWS Regions where SageMaker AI Projects is offered.

🧠 You can now run asynchronous batch AI inference inside Amazon OpenSearch Ingestion pipelines to enrich and ingest very large datasets for Amazon OpenSearch Service domains. The same AI connectors previously used for real-time calls to Amazon Bedrock, Amazon SageMaker, and third parties now support high-throughput, offline jobs. Batch inference is intended for offline enrichment scenarios—generating up to billions of vector embeddings—with improved performance and cost efficiency versus streaming inference. The feature is available in regions that support OpenSearch Ingestion on domains running 2.17+.

🛡️ Amazon SageMaker managed MLflow is now available in both AWS GovCloud (US-West) and AWS GovCloud (US-East) regions. The managed service integrates MLflow experiment tracking with SageMaker capabilities, streamlining AI experimentation and accelerating GenAI development from idea to production. It provides end-to-end observability to help reduce time-to-market and simplify compliance and operational oversight for government workloads.