Kategorie: Amazon AWS

Amazon SageMaker AI now supports instance preference lists for training and processing jobs

Today, Amazon SageMaker AI announces instance preference lists for training and processing jobs, making it easier and faster to find compute capacity for your workloads. Many AI training, fine-tuning, and data processing workloads run comparably well on any of several instance types or sizes. However, before now, you had to name only one instance type at the time of job submission and wait for SageMaker to find that specific instance for your job. For high-demand GPUs during peak periods, where wait times can be unpredictable, customers sometimes had to build complex retry logic or concurrently submit multiple jobs with different instance types to find the first available option. Now you can simply provide a prioritized list of the instance types your workload accepts, and SageMaker automatically runs your job on the first available configuration from your preferences. With this solution, your training or processing job will likely start sooner.
To use this feature, you specify your instance type and count preferences in priority order when submitting the training or processing job. For example, your list might contain a preference of two instances of ml.g6.48xlarge or four instances of ml.g5.48xlarge. SageMaker works through the list and launches your job on the first configuration where capacity is available. You can also configure the capacity sourcing from on-demand sources or from your reserved SageMaker Flexible Training Plans within the same job submission. This feature simplifies the process of getting compute for your jobs during high-demand periods and reduces the undifferentiated manual retrying you would otherwise do, all within the SageMaker training and processing job APIs you already use.
Instance preference lists for SageMaker training and processing jobs is available today in all AWS Regions where SageMaker is available through the SageMaker CLIs, APIs, SDKs and Console UI. To learn more, see our documentation or our launch blog.
Quelle: aws.amazon.com

AWS Glue zero-ETL adds target table property ownership and conflict detection

AWS Glue zero-ETL integrations now detect table property conflicts and track integration ownership. When you configure a source table and target catalog, Glue associates the resulting table properties with the owning integration, so two integrations can no longer be pointed at the same target table without your knowledge. This works across Amazon S3 Tables and SageMaker Lakehouse catalogs.
Data teams running multiple zero-ETL integrations gain predictable control over where each source table lands. If you attempt to create or modify an integration whose table properties are owned by another integration, Glue identifies the owning integration and guides you to choose a different target or update the existing one, so your pipelines stay isolated and your data lands exactly where you intend.
This feature is available in all AWS Commercial and AWS GovCloud (US) Regions where AWS Glue zero-ETL integrations are supported.
To learn more, refer to the AWS Glue Developer Guide and get started in the AWS Glue console.
Quelle: aws.amazon.com

granite-speech-4.1-2b, kanana-2-30b-a3b-instruct, and OpenFold3 models now available on Amazon SageMaker JumpStart

BM’s granite-speech-4.1-2b, Kakao’s kanana-2-30b-a3b-instruct, and the OpenFold Consortium’s OpenFold3 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning multilingual speech recognition, bilingual agentic AI, and biomolecular structure prediction, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
granite-speech-4.1-2b is purpose-built for multilingual automatic speech recognition (ASR) and bidirectional speech translation (AST) across English, French, German, Spanish, Portuguese, and Japanese. This compact 2B-parameter speech-language model delivers a word error rate of 5.33% with a real-time factor of ~231, making it one of the most efficient ASR models in its class. Released under Apache 2.0, it integrates seamlessly into enterprise voice workflows for transcription, translation, and audio processing at scale.
kanana-2-30b-a3b-instruct excels in bilingual Korean-English instruction following and agentic AI workflows. Developed by Kakao, it adopts a cutting-edge architecture featuring Multi-head Latent Attention (MLA) and Mixture-of-Experts (MoE), activating only 3B of its 30B total parameters per forward pass for superior throughput. Post-trained with supervised fine-tuning and reinforcement learning, it supports up to 128K tokens via YaRN scaling and is designed to function as an AI collaborator that understands context and acts proactively.
OpenFold3 provides all-atom biomolecular complex structure prediction for proteins, DNA, RNA, and small-molecule ligands. Developed by the OpenFold Consortium and the AlQuraishi Lab at Columbia University, this diffusion-based model extends structure prediction beyond single proteins to model multi-chain complexes and heterogeneous biomolecular interactions. It supports computer-aided drug design and is applicable across academic and pharmaceutical research labs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart

Google DeepMind’s Gemma-4-31B-it-assistant and NVIDIA’s Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring the flagship Gemma 4 31B dense architecture to enterprise workloads in both full-precision and optimized quantized variants, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Gemma-4-31B-it-assistant is built for multimodal reasoning, coding, and agentic workflows as the assistant-tuned variant of Google’s flagship 31B dense model. It handles text and image inputs (including video as frame sequences) and generates text output, with a 256K-token context window and support for over 140 languages. Ranked #3 among open models on the Arena AI text leaderboard—outcompeting models 20x its size—it features a hybrid attention mechanism interleaving local sliding-window and full global attention with native function calling for building autonomous agents.
Gemma-4-31B-IT-NVFP4 delivers the same Gemma 4 31B capabilities at a fraction of the memory footprint. Quantized with NVIDIA’s ModelOpt framework to 4-bit FP4 precision, it reduces memory usage to ~18.5 GB (68% smaller than the base model) and achieves approximately 2.5x faster inference while retaining 97–99% of the original model’s quality. Ideal for cost-efficient, high-throughput production deployments on NVIDIA RTX, DGX Spark, and data center GPUs.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models now available on Amazon SageMaker JumpStart

Mistral AI’s Ministral-3-3B-Instruct-2512 and Ministral-3-8B-Instruct-2512 models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models from the Ministral 3 family bring compact, vision-capable language models purpose-built for edge deployment and resource-constrained environments, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Ministral-3-3B-Instruct-2512 is engineered for ultra-lightweight edge deployment with multimodal understanding. Comprising a 3.4B language model and a 0.4B vision encoder, it fits in just 8GB of VRAM in FP8 while supporting a 256K-token context window. It offers vision analysis, multilingual instruction following across dozens of languages (including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic), strong system-prompt adherence, and native function calling with structured JSON output—all under the Apache 2.0 license.
Ministral-3-8B-Instruct-2512 delivers frontier-class capabilities comparable to its larger Mistral Small 3.2 24B counterpart in a compact 8B form factor. Built with an 8.4B language model and a 0.4B vision encoder, it fits in 12GB of VRAM in FP8 and features an interleaved sliding-window attention pattern for faster, memory-efficient inference. It shares the same vision, multilingual, agentic, and function-calling capabilities as its 3B sibling while offering stronger reasoning and generation performance.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Qwen3.6-35B-A3B-NVFP4 and Wan2.1-T2V-1.3B-Diffusers models now available on Amazon SageMaker JumpStart

NVIDIA’s Qwen3.6-35B-A3B-NVFP4 and Alibaba’s Wan2.1-T2V-1.3B-Diffusers models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning agentic coding with long-context reasoning and lightweight text-to-video generation, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.
These models address different enterprise AI challenges with specialized capabilities:
Qwen3.6-35B-A3B-NVFP4 is optimized for agentic coding, multimodal reasoning, and long-context understanding as the NVIDIA-quantized variant of Alibaba’s Qwen3.6-35B-A3B. This Mixture-of-Experts model contains 35B total parameters with only 3B activated per token (8 of 256 experts), supporting a 262K-token context window extendable to ~1M via YaRN scaling. Quantized to NVFP4 using NVIDIA’s ModelOpt framework, it preserves thinking across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines—all at a significantly reduced memory footprint.
Wan2.1-T2V-1.3B-Diffusers excels in text-to-video generation on consumer-grade hardware. Built on the diffusion transformer paradigm with a novel Video Variational Autoencoder (VAE), this 1.3B-parameter model generates high-quality, physics-consistent video clips from text prompts while requiring only 8.19 GB of VRAM. It can produce a 5-second 480p video on an RTX 4090 in approximately 4 minutes, making it one of the most accessible open-source video generation models available.
With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases.
To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

AWS Elemental MediaLive enables frame-accurate pipeline locking for streams without timecode

AWS Elemental MediaLive now supports Video Aligned Locking, a new feature to synchronize video pipelines without requiring timecode from the source. Previously, achieving frame-accurate locking across video outputs required investing in specialized hardware or managing complex, external synchronization workflows. While timecode is generally standard for highly produced, traditional broadcast content, it is often unavailable and difficult to manage in typical digital streaming workflows. The new capability uses visual signatures to automatically identify and align specific frames across multiple video streams, enabling customers to achieve frame-accurate input switching across standard pipeline channels, as well as linked, cross-region single-pipeline channels.
Video Aligned Locking supports outputs including HLS, MediaPackage, CMAF Ingest, UDP and SRT. To learn more about how to configure this feature, visit the Requirements for Video Aligned Locking documentation. For a deeper dive into channel synchronization, visit the guide on Implementing Pipeline Locking.
Quelle: aws.amazon.com

AWS DevOps Agent adds support for bidirectional Slack communication

AWS DevOps Agent now enables engineers to manage production operations across AWS, multicloud, and on-premises environments with the complete investigation lifecycle directly within Slack. Previously, on-call engineers and DevOps teams faced fragmented workflows during high-severity incidents, having to context-switch between communication and investigation platforms. This update consolidates both surfaces into a single, unified location where incident response already happens.
Engineering teams can now initiate and steer investigations by simply @mentioning AWS DevOps Agent in any connected Slack private channel. All investigation activity, including team-contributed context, agent findings, and recommended actions, is captured within a single thread. Ask questions about AWS resources, system metrics, alarm status, deployment history, and incident patterns, reducing cognitive load during critical moments and making production operations significantly more efficient.
This capability is available in all commercial AWS Regions where AWS DevOps Agent is currently supported.
To get started, read the user guide. See all the latest AWS DevOps Agent features on the release history page.
Quelle: aws.amazon.com

AWS Lambda now supports direct read configuration for Amazon S3 Files

AWS Lambda now supports direct read configuration for Amazon S3 Files, letting you configure which storage your functions read from: S3 Files high-performance storage or your S3 bucket. With this launch, you can optimize the throughput and latency of file reads for your Lambda functions based on your application requirements.
Customers use S3 Files with Lambda functions to build scalable data processing pipelines and stateful agentic workloads with the performance and simplicity of a file system, while benefiting from the scalability, durability, and cost-effectiveness of S3. S3 Files serves data from high-performance storage for low latency or directly from your S3 bucket for high throughput on large reads, automatically routing each operation to the storage best suited for it. By default, Lambda supports direct reads from your S3 bucket only for functions configured with 512 MB of memory or higher. However, without control over the direct read configuration, you cannot optimize read performance for your specific application requirements. With this launch, you can explicitly enable or disable direct read for S3 Files on your Lambda functions, independent of function memory size. When you enable direct read, your function reads files 1 MB or larger directly from your S3 bucket for maximum throughput, and smaller files are served through the high-performance storage. When you disable it, all reads are served through the high-performance storage for the lowest latency.
This capability is available in all AWS commercial Regions, AWS GovCloud (US-East), and AWS GovCloud (US-West) Regions, except Asia Pacific (New Zealand), Middle East (Bahrain), and Middle East (UAE). You can configure direct read for S3 Files using the AWS Management Console, AWS CLI, AWS SDKs, and AWS CloudFormation. There is no additional charge beyond standard Lambda and S3 Files pricing. To learn more about how to use S3 Files with your Lambda function, visit the AWS Lambda developer guide.
Quelle: aws.amazon.com

AWS HealthOmics now publishes real-time run metrics to Amazon CloudWatch

AWS HealthOmics now publishes real-time run metrics to Amazon CloudWatch, giving you visibility into workflow resource utilization as runs execute. The 14 new run metrics span CPU and GPU usage, memory usage, file system usage and I/O metrics, network throughput, and ephemeral storage usage. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
Real-time run metrics can help you identify CPU or GPU bottlenecks, detect memory or storage exhaustion before a task fails, and track file system throughput, all without opening a support case. By comparing actual usage against allocated resources, you can right-size the compute and storage configurations for your workflows. These metrics are emitted using the Amazon CloudWatch OpenTelemetry standard, so you can integrate them with third-party observability tools in addition to native CloudWatch dashboards and alarms.
Real-time run metrics are now available in the following AWS HealthOmics Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), and Asia Pacific (Singapore, Seoul, Tokyo). To learn more, visit the Monitoring run metrics with CloudWatch documentation. Amazon CloudWatch charges for these run metrics based on the volume of metric data ingested. For more information on pricing, visit Amazon CloudWatch pricing.
Quelle: aws.amazon.com