Artificial Intelligence
Category: Announcements
Enhanced performance for Amazon Bedrock Custom Model Import
You can now achieve significant performance improvements when using Amazon Bedrock Custom Model Import, with reduced end-to-end latency, faster time-to-first-token, and improved throughput through advanced PyTorch compilation and CUDA graph optimizations. With Amazon Bedrock Custom Model Import you can to bring your own foundation models to Amazon Bedrock for deployment and inference at scale. In this post, we introduce how to use the improvements in Amazon Bedrock Custom Model Import.
Amazon SageMaker AI introduces EAGLE based adaptive speculative decoding to accelerate generative AI inference
Amazon SageMaker AI now supports EAGLE-based adaptive speculative decoding, a technique that accelerates large language model inference by up to 2.5x while maintaining output quality. In this post, we explain how to use EAGLE 2 and EAGLE 3 speculative decoding in Amazon SageMaker AI, covering the solution architecture, optimization workflows using your own datasets or SageMaker’s built-in data, and benchmark results demonstrating significant improvements in throughput and latency.
HyperPod now supports Multi-Instance GPU to maximize GPU utilization for generative AI tasks
In this post, we explore how Amazon SageMaker HyperPod now supports NVIDIA Multi-Instance GPU (MIG) technology, enabling you to partition powerful GPUs into multiple isolated instances for running concurrent workloads like inference, research, and interactive development. By maximizing GPU utilization and reducing wasted resources, MIG helps organizations optimize costs while maintaining performance isolation and predictable quality of service across diverse machine learning tasks.
Claude Opus 4.5 now in Amazon Bedrock
Anthropic’s newest foundation model, Claude Opus 4.5, is now available in Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models from leading AI companies. In this post, I’ll show you what makes this model different, walk through key business applications, and demonstrate how to use Opus 4.5’s new tool use capabilities on Amazon Bedrock.
Announcing the AWS Well-Architected Responsible AI Lens
Today, we’re announcing the AWS Well-Architected Responsible AI Lens—a set of thoughtful questions and corresponding best practices that help builders address responsible AI concerns throughout development and operation.
Accelerate enterprise solutions with agentic AI-powered consulting: Introducing AWS Professional Service Agents
I’m excited to announce AWS Professional Services now offers specialized AI agents including the AWS Professional Services Delivery Agent. This represents a transformation to the consulting experience that embeds intelligent agents throughout the consulting life cycle to deliver better value for customers.
Build reliable AI systems with Automated Reasoning on Amazon Bedrock – Part 1
Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at […]
Introducing Amazon Bedrock cross-Region inference for Claude Sonnet 4.5 and Haiku 4.5 in Japan and Australia
こんにちは, G’day. The recent launch of Anthropic’s Claude Sonnet 4.5 and Claude Haiku 4.5, now available on Amazon Bedrock, marks a significant leap forward in generative AI models. These state-of-the-art models excel at complex agentic tasks, coding, and enterprise workloads, offering enhanced capabilities to developers. Along with the new models, we are thrilled to announce that […]
Iterative fine-tuning on Amazon Bedrock for strategic model improvement
Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and hoping the results meet expectations without the ability to make incremental adjustments. Single-shot fine-tuning frequently leads to suboptimal results and requires starting the entire process from scratch when improvements are […]
Transforming the physical world with AI: the next frontier in intelligent automation
In this post, we explore how Physical AI represents the next frontier in intelligent automation, where artificial intelligence transcends digital boundaries to perceive, understand, and manipulate the tangible world around us.








