AI

Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the creation of proprietary intellectual property. A […]

In dentistry, image quality determines whether a claim is paid or denied. Up to 20 percent insurance claims are initially denied, with missing or low-quality images among the leading causes. Yet quality assessment has traditionally been a manual, after-the-fact process. A clinician reviews an X-ray hours or days after capture, discovering problems only when a

In this post we show how to build a semantic layer on AWS using Stardog’s Semantic AI Application over Amazon Aurora and Amazon Redshift, and how to run a Strands Agents agent on Amazon Bedrock AgentCore that queries the layer to answer customer 360 questions across both sources without extract, transform, and load (ETL). The

An artificial intelligence (AI) agent can process an invoice, help adjudicate a claim, or classify a support ticket in a proof of concept. But running these agents across thousands or even millions of work items in a production environment introduces an entirely different set of challenges. At enterprise scale, success depends on much more than

This post was co-written with Daniel Han and Michael Han from Unsloth. Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the numerical precision of a model’s weights

In this post, learn how KTern.AI, an SAP digital transformation platform, used Amazon Bedrock AgentCore to build and deploy AI agents ready for enterprise-scale SAP transformation workloads. These agents autonomously orchestrate workflows from reverse engineering, fit-to-standard, and code analysis to exception mining in Finance and Sales processes. The result is automation without custom agent infrastructure.

When prefill and decode share a GPU, long prompts stall token generation for every concurrent request. Disaggregated Prefill and Decode (DPD) removes this interference by running each phase on separate GPU pools connected through Elastic Fabric Adapter (EFA) with Remote Direct Memory Access (RDMA). Large language model (LLM) inference has two fundamentally different phases. Prefill

Billed as the “world’s first museum of AI arts,” Dataland uses wearables and troves of material from the Amazon to merge nature, biometrics, and art.

Amid live coding sessions and Silicon Valley optimism, the UN’s AI for Good summit wrestled with an increasingly urgent question: Can global governance catch up before the technology races beyond its control?

The move comes after Simo took significant medical leave. Simo will stay on as a part-time advisor.