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 […]
Launching UI for generative AI inference recommendations in Amazon SageMaker AI Read More »
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
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.
Real-time dental image verification with Amazon SageMaker AI at Henry Schein One Read More »
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
Build a semantic layer for agentic AI on AWS with Stardog and Amazon Bedrock AgentCore Read More »
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
Scaling agentic workflows with native case management in Amazon Quick Automate Read More »
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
Deploying quantized models on Amazon SageMaker AI with Unsloth Read More »
When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the
MCP tool design: Practical approaches and tradeoffs Read More »
As enterprises scale their generative AI workloads, the demand for faster, more observable, and more flexible inference infrastructure continues to grow. Amazon SageMaker HyperPod is rising to meet that challenge
Enterprises deploying Claude Code and Claude Desktop across development teams need centralized control over access, cost, and policy. At scale, this is hard to manage: each developer needs an individual
Introducing Claude apps gateway for AWS Read More »
In pharmaceutical research, scientists face a fundamental challenge: accessing and connecting the vast amount of scientific knowledge scattered across disparate systems. From published literature and internal lab notes to genomics
