AWS Machine Learning
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AWS Machine LearningLabsBeyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch Most large language models (LLMs) use transformer architectures with softmax attention. During inference, their key-value (KV) cache grows with the conversation or document context.
AWS Machine LearningLabsWork management context for AI agents with Smartsheet MCP on AWS AI agents are most useful when they can act on the real work happening across your organization. Work management involves complex, cross-functional tasks such as updating project timelines…
AWS Machine LearningLabsFrom data to decisions: Conversational supply chain intelligence with Amazon Quick and Amazon Bedrock AgentCore Giving supply chain teams direct access to the systems behind their decisions. Every supply chain generates constant signals: orders in approval, shifting lead times, slowing ports, drifting prices…
AWS Machine LearningLabsFine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod GPT-OSS-20B is an open-weight reasoning model that thinks in English. Ask it to reason in French, German, Spanish, or Italian and it acknowledges the instruction, then produces its chain-of-thought…
AWS Machine LearningLabsHow Octave built a secure multi-tenant AI foundation on Amazon Bedrock In quality management for regulated industries, the institutional memory that resolves a problem is almost always buried somewhere in a customer’s history.
AWS Machine LearningLabsAccess OpenAI GPT models on Amazon Bedrock from Canada with cross-Region inference Canadian organizations building generative AI applications on Amazon Bedrock often need the latest foundation models (FMs), such as OpenAI GPT-6.1, OpenAI GPT-6, and GPT-5.6, accessible from the…
AWS Machine LearningLabsDiscover tools at runtime with AWS Agent Registry Runtime discovery with AWS Agent Registry can help address a problem that agent demos might hide. Hardcoded tools work fine for a handful, start to degrade at a few dozen, and become unmanageable…
AWS Machine LearningLabsSecuring AI agent identity with X.509 PKI on AWS When AI agents access databases, APIs, and sensitive business operations, establishing cryptographic identity through X.509 public key infrastructure (PKI) becomes a critical requirement.
AWS Machine LearningLabsHow Eon built multi-cloud investigation with an MCP Gateway on Amazon Bedrock AgentCore This is a guest post co-written with Roei Zavida from Eon. Independent software vendors (ISVs) building AI-powered products need to connect agents and humans to dozens of operational tools across…
AWS Machine Learning
AWS Machine LearningLabsBuild a semantic layer for agentic AI on AWS with Denodo and Amazon Bedrock AgentCore Agentic AI applications need governed access to live enterprise data. That data can live in operational systems, analytical warehouses, SaaS applications, other clouds, or on premises.
AWS Machine Learning
AWS Machine LearningLabsBuild a semantic layer for agentic AI on AWS with Strategy Mosaic and Amazon Bedrock AgentCore This post is co-written with Zhili Cheng, Principal Product Manager, George Earle, Director, AI Engineering, and Kyler K, Sales Engineer of Strategy.
AWS Machine LearningLabsBuild a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore In the first post in this series , we built a semantic layer on AWS using Stardog’s knowledge graph over Amazon Aurora and Amazon Redshift .
AWS Machine LearningLabsStructured outputs for Anthropic Claude models on Amazon Bedrock Amazon Bedrock now supports structured outputs for all Anthropic Claude models, on both the bedrock-runtime and bedrock-mantle endpoints, where the model is available.
AWS Machine LearningLabsDevelopers can now access SageMaker Studio through the new AWS experience Today, we’re excited to announce that new Amazon SageMaker AI customers can now land inside a fully configured Amazon SageMaker Studio environment in only a few steps after signing up for AWS.
AWS Machine LearningLabsAmazon SageMaker JumpStart: 2026 year-to-date in review Amazon SageMaker JumpStart is a machine learning (ML) hub within Amazon SageMaker AI that provides access to a curated catalog of AI models, solution templates, and example notebooks.
AWS Machine LearningLabsPegasus 1.5 from TwelveLabs is now available in Amazon Bedrock On Oct 9, 2026 we announced the availability of TwelveLabs’ Pegasus 1.5 on Amazon Bedrock . Pegasus 1.5 is a video language model that turns long-form video into structured, timestamped metadata.
AWS Machine LearningLabsHow Deepgram optimized voice AI at scale with AWS Trainium As voice becomes a first-class modality for AI, Deepgram’s models span speech-to-text, text-to-speech, and voice agents.
AWS Machine LearningLabsPush-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM vLLM is an open-source inference and serving engine for large language models (LLMs). LLM inference has two distinct phases: prefill, which processes the input prompt and builds the key-value (KV)…
AWS Machine LearningLabsImprove TTFT for multi-turn agentic workloads with bi-directional KV transfer in vLLM Multi-turn agentic workloads build on accumulated context as an agent reasons, invokes tools, and incorporates earlier results.
AWS Machine LearningLabsBuilding trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore The North Star Every organization racing to deploy AI agents faces the same reckoning: an agent that demos beautifully can quietly degrade in production, and by the time a human notices, the trust is…
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