New experts join Google’s AI & Economy team
We are expanding our AI & Economy team with world-class academic advisors, fellows, and core internal researchers.
New experts join Google’s AI & Economy team Read More »
We are expanding our AI & Economy team with world-class academic advisors, fellows, and core internal researchers.
New experts join Google’s AI & Economy team Read More »
Eliminate GPU waste. Reduce first-token latency by up to 82%. Install one Kubernetes-native addon with zero application changes. The problem: Naive routing wastes your most expensive resource Running large language models (LLMs) at scale on GPU clusters is expensive. The default Kubernetes load balancers are making it worse. Round-robin and least-connections algorithms have no visibility
Introducing Amazon SageMaker HyperPod Inference Gateway Read More »
Google worked side-by-side with designers Jane Wade and Sergio Hudson to custom-design Google Flow tools to prep for NYFW.
Co-creating the future of fashion with Google Read More »
Google and the UN system have launched the UN System Data Commons, a new open platform making global statistics accessible and easy to search.
Making global data easier to explore Read More »
Hiring at scale in industries such as retail, logistics, hospitality, and others has its fair share of challenges. Recruiting teams are expected to fill hundreds of roles within tight timelines, often with limited capacity and with tools that weren’t designed to seamlessly work together. As a result of this, applications pile up, phone screens get
Reduce time-to-hire for quality candidates with AI-powered Amazon Connect Talent Read More »
When building a Retrieval Augmented Generation (RAG) solution with Amazon Bedrock Knowledge Bases, selecting the right vector store impacts performance and cost. Amazon Bedrock Knowledge Bases offers a fully managed option and a customer-managed option where you choose your own vector store. This post focuses on the customer-managed path, comparing the three supported backends: Amazon
Selecting a vector store for Amazon Bedrock Knowledge Bases Read More »
Git activity is one of the richest signals engineering teams produce that can provide continuous observability into development analytics. The challenge is extracting these Git metrics at scale, which has traditionally required hand-rolled extract, transform, and load (ETL) jobs, dedicated infrastructure, and ongoing maintenance. Further, with modern development tools becoming more prevalent, teams need a
A serverless, data-driven Git metrics dashboard using Amazon Quick Sight Read More »
Building a working agentic prototype takes an afternoon. Getting it to production is where the work explodes. The moment an agent has to serve more than one user, a new layer of engineering appears and it’s critical to tell whether the agent is doing the right thing on real traffic. Concurrency, session isolation, identity, persistent
A shared agentic platform for Wood Mackenzie, on Amazon Bedrock AgentCore Read More »
Basic AI chat isn’t enough for financial services organizations that need secure, self-service AI agents. In financial services, employees need AI that can work with internal systems and sensitive client data, stay inside a governed environment, and remain auditable and cost-transparent. All of this must happen without every team standing up its own tools. This
How MRH Trowe enabled secure self-service AI agents in financial services Read More »
Each Model Context Protocol (MCP) tool invocation on Amazon Quick is an access event that can require defense-in-depth authorization at the tool and parameter level. This applies in addition to a valid token. Without granular controls, a single misconfigured permission can bypass the access requirements that organizations might need to fulfill for compliance purposes. In
Implementing defense-in-depth authorization for MCP tools on Amazon Quick Read More »