All news with #amazon sagemaker tag

Amazon SageMaker enables self-service notebook migration

🔁 Amazon SageMaker Notebook instances now support self-service migration via the PlatformIdentifier parameter in the UpdateNotebookInstance API. You can update unsupported platform identifiers (notebook-al1-v1, notebook-al2-v1, notebook-al2-v2) to supported versions (notebook-al2-v3, notebook-al2023-v1) while preserving data and configurations. The capability is available through AWS CLI (v2.31.27+) and SDKs in all Regions where Notebook instances are supported. This simplifies keeping instances current and reduces manual migration effort.

AWS SageMaker AI adds serverless model customization

🚀 Amazon SageMaker AI now offers a serverless model customization capability that lets developers quickly fine-tune popular models using supervised learning, reinforcement learning, and direct preference optimization. The fully managed, end-to-end workflow simplifies data preparation, synthetic data generation, training, evaluation, and deployment through an easy-to-use interface. Supported base models include Amazon Nova, Llama, Qwen, DeepSeek, and GPT-OSS. The AI agent-guided workflow is in preview with regional availability and a waitlist.

AWS AI Factories: Dedicated High-Performance AI Infrastructure

🚀 AWS AI Factories are now available to deploy high-performance AWS AI infrastructure inside customer data centers, combining AWS Trainium, NVIDIA GPUs, low-latency networking, and optimized storage. The service integrates Amazon Bedrock and Amazon SageMaker to provide immediate access to foundation models without separate provider contracts. AWS manages procurement, setup, and operations while customers supply space and power, enabling isolated, sovereign deployments that accelerate AI initiatives.

Amazon SageMaker Catalog Exports Asset Metadata to Iceberg

🔍 Amazon SageMaker Catalog now exports asset metadata as an Apache Iceberg table via Amazon S3 Tables, enabling teams to query catalog inventory with standard SQL without building custom ETL. The export includes technical fields (resource_id, resource_type), business metadata (asset_name, business_description), ownership details, and timestamps, partitioned by snapshot_date for time travel queries. The dataset appears in SageMaker Unified Studio and is queryable from Amazon Athena, Studio notebooks, AI agents, and BI tools. Available in all supported Regions at no additional SageMaker charge; you pay for S3 Tables storage and Athena queries.

Amazon SageMaker AI Adds Serverless MLflow Support

🧠 Amazon SageMaker AI now offers a serverless MLflow capability that automatically scales to support experiment tracking and model development without infrastructure setup. The service scales up for demanding workloads and scales down during idle periods, reducing operational overhead. Administrators can enable cross-account access via Resource Access Manager (RAM). The feature integrates with SageMaker AI JumpStart, Model Registry, and Pipelines and is offered at no additional charge in select AWS Regions.

AWS AI League 2026 Championship Expands Challenges

🤖 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.

SageMaker HyperPod Adds Custom Kubernetes Labels and Taints

🛠️ 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.

AWS Adds Warm Storage Tier to Kinesis Video Streams

📦 AWS announced a new warm storage tier for Amazon Kinesis Video Streams, offering lower-cost storage for extended media retention while preserving sub-second access latency. The existing standard tier is now designated the hot tier and remains optimized for real-time, short-term use. Developers can configure fragment sizes to trade latency for ingestion cost, and both tiers integrate with Amazon Rekognition Video and Amazon SageMaker for continuous video analytics. The warm tier is available in all supported regions except AWS GovCloud (US).

AWS Adds Apache Iceberg V3 Deletion Vectors and Lineage

🔔 AWS now supports Apache Iceberg V3 deletion vectors and row lineage across key analytics services. These features — available in Amazon EMR 7.12, AWS Glue, SageMaker notebooks, Amazon S3 Tables, and the AWS Glue Data Catalog — accelerate data modifications and make it simpler to identify changed records. Enable V3 by setting the table property 'format-version = 3' in CREATE TABLE or by updating table metadata; supported AWS query engines will automatically use deletion vectors and row lineage.

SageMaker AI Inference Adds Bidirectional Streaming

🎙️ 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 Athena for Apache Spark Integrated with SageMaker

🚀 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 One-Click Onboarding for Existing Data

✨ 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 for Analytics and ML Development

🤖 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 notebooks with built-in AI agent experience

🤖 Amazon SageMaker introduces a serverless notebook experience that consolidates SQL, Python, and natural-language workflows into a single interactive workspace for analytics and ML. The environment is backed by Amazon Athena for Apache Spark to scale from interactive queries to petabyte-scale processing without pre-provisioned infrastructure. A built-in AI agent generates code and SQL from natural-language prompts to accelerate development, and the feature is available via SageMaker Unified Studio's one-click onboarding in multiple AWS Regions.

Amazon SageMaker HyperPod Adds IDE and Notebook Support

🚀 Amazon SageMaker HyperPod now supports running IDEs and Notebooks on persistent EKS-based HyperPod clusters, allowing developers to run JupyterLab, Code Editor, or connect local IDEs directly to GPU-backed compute. Developers can share data across interactive sessions and training jobs via mounted file systems such as FSx and EFS, and use familiar tools including the HyperPod CLI. Administrators gain unified governance through HyperPod Task Governance and visibility into CPU, GPU, and memory consumption via HyperPod Observability, helping optimize cluster utilization. The feature is available in all AWS Regions that support HyperPod, excluding China and GovCloud (US).

AWS Glue Adds Zero-ETL Support for More SAP Entities

🔄 AWS Glue now provides full snapshot and incremental zero-ETL ingestion for additional SAP entities. The update adds snapshot ingestion for entities without deletion tracking and timestamp-based incremental loads for non-ODP systems, extending existing ODP support. Organizations can ingest SAP data directly into Amazon Redshift or the lakehouse architecture used by Amazon SageMaker, reducing engineering effort and operational complexity. This feature is available in all Regions where AWS Glue zero-ETL is offered.

Amazon SageMaker Studio Integrates EMR on EKS with SSO

🔒 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.

SageMaker Studio: Long‑Running Sessions with Corporate IDs

⏳ 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 Catalog Adds Column-Level Metadata

📣 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 Enforces Glossary Metadata

📌 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.