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Enterprise data with tens of millions of rows, row-level and column-level security, and dozens of datasets spanning multiple business domains need AI-generated answers that are trustworthy, reproducible, and fast, while respecting governance rules consistently. With foundation models (FMs), organizations can build systems that work well for small datasets where a business user asks a question …

Amazon Quick: Accelerating the path from enterprise data to AI-powered decisions Read More »

This week, the new, AI-powered Google Finance is launching across Europe, with full local language support. This reimagined experience offers a suite of powerful capabil… ​ 

Today we’re launching The Small Brief, an initiative bringing together four ad industry icons to champion a local businesses they love. Their mission is to build breakth… ​ 

Seismic data analysis is an essential component of energy exploration, but configuring complex processing workflows has traditionally been a time-consuming and error-prone challenge. Halliburton’s Seismic Engine, a cloud-native application for seismic data processing, is a powerful tool that previously required manual configuration of approximately 100 specialized tools to create workflows. This process was not only …

Halliburton enhances seismic workflow creation with Amazon Bedrock and Generative AI Read More »

As companies of various sizes adopt graphic processing units (GPU)-based machine learning (ML) training, fine-tuning and inference workloads, the demand for GPU capacity has outpaced industry-wide supply. This imbalance has made GPUs a scarce resource, creating a challenge for customers who need reliable access to GPU compute resources for their ML workloads. When you encounter …

Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans Read More »

Training large language models requires accurate feedback signals, but traditional reinforcement learning (RL) often struggles with reward signal reliability. The quality of these signals directly influences how models learn and make decisions. However, creating robust feedback mechanisms can be complex and error prone. Real-world training scenarios often introduce hidden biases, unintended incentives, and ambiguous success …

Overcoming reward signal challenges: Verifiable rewards-based reinforcement learning with GRPO on SageMaker AI Read More »

We’re in the midst of a fundamental shift in how software gets built and used. AI agents are moving beyond assistants that wait for instructions. They call APIs, access MCP servers, coordinate with other agents, and complete complex multi-step tasks on behalf of users. As agents take on increasingly diverse tasks, the ecosystem around them …

Agents that transact: Introducing Amazon Bedrock AgentCore Payments, built with Coinbase and Stripe Read More »

We’ve rounded up the top ways you can use Google’s AI Mode, Search Live and Shopping to help your plants thrive. ​ 

Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. Furbo combines smart cameras with AI to detect behaviors such as barking, running, or unusual activity, and alerts owners in real time. At the core of this capability are computer vision and vision-language models that …

Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2 Read More »

Hapag-Lloyd stands as one of the world’s leading liner shipping companies, operating a modern fleet of 313 container ships with a total transport capacity of 2.5 million TEU (Twenty-foot Equivalent Unit—a standard unit of measurement for cargo capacity in container shipping). The company maintains a container capacity of 3.7 million TEU, which includes one of …

How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights Read More »