AI

Deploying generative AI models to production requires finding the right combination of instance type, serving container with settings, and optimization strategy. This process typically requires a long iteration cycle of optimization and manual benchmarking. In April 2026, Amazon SageMaker AI launched this inference recommendations, so customers can programmatically get data-driven, production-ready configurations through APIs. This […]

With the exploding popularity of generative artificial intelligence, many open-source models are now available online for anyone to adapt for their task, such as generating product renderings in a certain artistic style.  But these models also find their way into the hands of nefarious actors who may optimize them to produce illegal content, like hate

Researchers cobbled together funding and time to show how quantum computing could aid in the development of drugs to help underserved populations and combat rare diseases.

Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.

The iPhone-maker claims OpenAI encouraged poached employees to bring over confidential presentations, secret prototypes, and key supplier details.

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