Tag: SysAdmin

AWS  now offers Claude Opus 4.8 -- Anthropic's most capable generally available model to date -- delivering meaningful advances across agentic coding, professional knowledge work, and long-running autonomous tasks for developers and enterprises building production AI applications. Claude Opus 4.8 can perform longer autonomous runs, deeper reasoning, and consistency to be trusted with production work. For coding, the Opus 4.8 reads codebases like an engineer, plans before it edits, and holds context across long sessions in real repositories. For agentic tasks, it is better at finding paths around obstacles instead of stalling, recovering from its own errors, and knowing when to ask for help versus when to keep going. For knowledge work, it better synthesizes across long documents and complex sources, self-checks its output, and delivers structured deliverables that hold up to review. Customers have two ways to access Claude Opus 4.8: Amazon Bedrock and Claude Pla

Amazon DynamoDB Streams now supports AWS PrivateLink for FIPS (Federal Information Processing Standard) endpoints in AWS GovCloud (US) Regions. DynamoDB Streams captures time-ordered sequences of item-level modifications in DynamoDB tables, enabling real-time data processing and event-driven architectures. This enhancement allows government agencies and organizations with federal compliance requirements to establish private connectivity between their VPCs and DynamoDB Streams FIPS endpoints without exposing traffic to the public internet. This capability helps customers meet strict federal compliance and regulatory requirements while simplifying their network architecture. By keeping all traffic within the AWS network infrastructure, organizations can securely process real-time data streams, implement compliant change data capture (CDC) solutions, and build event-driven architectures that adhere to federal security standards. Government agencies operating in GovCloud regions

We are pleased to announce general availability of Amazon EC2 P4de instances in Asia Pacific (Tokyo) on SageMaker notebook instances. Amazon EC2 P4de instances are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, 2X higher than the GPUs in our current P4d instances. The new P4de instances provide a total of 640GB of GPU memory, which provide up to 60% better ML training performance along with 20% lower cost to train when compared to P4d instances. The improved performance will allow customers to reduce model training times and accelerate time to market. Increased GPU memory on P4de will also benefit workloads that need to train on large datasets of high-resolution data. Visit developer guides for instructions on setting up and using JupyterLab and

We are pleased to announce general availability of Amazon EC2 P5.48xl instances in Asia Pacific (Tokyo) on SageMaker notebook instances. Amazon EC2 P5.48xl instances are powered by NVIDIA H100 Tensor Core GPUs and deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce cost to train ML models by up to 40%. Customers can use P5 instances for training and deploying complex large language models (LLMs) and diffusion models powering generative AI applications. These applications include question answering, code generation, video and image generation, and speech recognition. Visit developer guides for instructions on setting up and using JupyterLab

We are pleased to announce general availability of Amazon EC2 P6-B200 instances in AWS US East (N. Virginia) on SageMaker notebook instances. Amazon EC2 P6-B200 instances are powered by 8 NVIDIA Blackwell GPUs with 1440 GB of high-bandwidth GPU memory and 5th Generation Intel Xeon processors (Emerald Rapids). These instances deliver up to 2x better performance compared to P5en instances for AI training. Customers can use P6-B200 instances to interactively develop and fine-tune large foundation models, including LLMs, mixture of experts models, and multi-modal reasoning models. These instances enable efficient experimentation with larger models directly in JupyterLab or CodeEditor environments for generative AI applications such as enterprise copilots and content generation across text, images, and video. Visit developer guides for instructions on setting up and using

Amazon Bedrock is a fully managed service that provides secure, enterprise-grade access to high-performing foundation models from leading AI companies, enabling you to build and scale generative AI applications. Amazon Bedrock customers can now view inference quotas for the bedrock-mantle endpoint through AWS Service Quotas. This gives customers a familiar, consistent way to track limits for this endpoint, the same way they already do for the bedrock-runtime endpoint and other AWS services, and gives them clear visibility into the limits that apply to their workloads. The bedrock-mantle endpoint supports the OpenAI Responses API, OpenAI Chat Completions API, and the Anthropic Messages API, letting customers run existing OpenAI or Anthropic based applications on Amazon Bedrock with minimal code changes. AWS Service Quotas now exposes per-model input-tokens-per-minute and output-tokens-per-minute quotas for supported models on the endpoint. With this launch, customer

AWS Elemental Inference now supports smart subtitles, a new AI-powered feature that automatically generates real-time subtitles for live video streams. Smart subtitles use advanced speech recognition to transcribe spoken audio and deliver Timed Text Markup Language (TTML)-formatted subtitles with low latency, helping broadcasters and streamers provide accessible content to viewers without manual captioning workflows or third-party services. With Smart subtitles, you can add live subtitling for content that is English (United States, Great Britain, and Australian), French, German, Italian, Portuguese, and Spanish to your broadcasts by enabling the feature through the native integration with AWS Elemental MediaLive. You can improve transcription accuracy for specialized content—such as sports commentary with athlete names or technical terminology—by creating custom dictionaries through the AWS Elemental Inference API or console. Smart subtitles work alongside existing Elem

We are pleased to announce general availability of Amazon EC2 P5.4xl instances on SageMaker notebook instances. Amazon EC2 P5.4xl instances are powered by NVIDIA H100 Tensor Core GPUs and deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce cost to train ML models by up to 40%. Customers can use P5 instances for training and deploying complex large language models (LLMs) and diffusion models powering generative AI applications. These applications include question answering, code generation, video and image generation, and speech recognition. Amazon EC2 P5.4xl instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Mumbai, Tokyo, Jakarta) and South Ame

We are pleased to announce general availability of Amazon EC2 P5en.48xl instances on SageMaker notebook instances. Amazon EC2 P5en instances feature 8 H200 GPUs which have 1.7x GPU memory size and 1.4x GPU memory bandwidth than H100 GPUs featured in P5 instances. P5en instances pair the H200 GPUs with high performance custom 4th Generation Intel Xeon Scalable processors, enabling Gen5 PCIe between CPU and GPU which provides up to 4x the bandwidth between CPU and GPU and boosts AI training and inference performance. P5en, with up to 3200 Gbps of third generation of EFA using Nitro v5, shows up to 35% improvement in latency compared to P5 that uses the previous generation of EFA and Nitro. This helps improve collective communications performance for distributed training workloads such as deep learning, generative AI, real-time data processing, and high-performance computing (HPC) applications. Amazo

Amazon EMR now supports Apache Spark 4.0.2 across all three deployment models. With Spark 4.0.2, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, enforce fine-grained access control (FGAC) at the row level or column level, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. With Spark 4.0.2, you can build data pipelines, making data engineering accessible to a broader range of users through standard ANSI SQL support, eliminating the need to learn Spark-specific syntax. Spark 4.0.2 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can enforce fine-grained access control (FGAC) on both read and write operations for AWS Lake Formation registered tables in your Apache Spark jobs. Building on these security capabilities, Apache Iceberg v