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In-House LLM Serving at Netflix

By AI Platform’s Model Runtime team and Inference team Introduction Most organizations consume LLMs through hosted APIs. Netflix went further — we run the full stack ourselves, from model deployment through inference, inside our existing production environment rather than a separate ML silo. Some of those decisions weren’t obvious, and a few revealed their trade-offs only under …

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Transform your sales organization with Amazon Quick: your new agentic AI teammate

The average sales rep spends only 40% of their time actually selling. The rest is eaten up by necessary, but lower-value work: customer relationship management (CRM) updates, prospect research, email drafting, and endless context-switching between tools. Before you know it, the day has passed and you’ve spent only a small fraction of your time on …

13 hands-on demos to build on Gemini Enterprise Agent Platform

Earlier this year, we introduced Gemini Enterprise Agent Platform, where you can build, scale, govern, and optimize agents. Today, we’re sharing 13 demos that walk you through what Agent Platform can do. Each one teaches a concept, a pattern, or an architecture you can put to work immediately. The best part? You don’t have to …

Location-Invariant Properties of Functions Versus Properties of Distributions: United in Testing but Separated in Verification

A property of functions is called location-invariant (or symmetric) if it can be characterized in terms of the frequencies in which each value occurs in the function, regardless of the locations in which each value occurs. It is known that the (query) complexity of testing location-invariant properties of functions is closely related to the (sample) …

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Build enterprise search for agents with Amazon Bedrock Managed Knowledge Base

Knowledge bases that ground agents and generative AI applications over your enterprise data are hard to build at scale. Teams typically stitch together connectors, parsers, vector stores, knowledge graphs, and retrieval logic, then operationalize all of it for production. Each piece brings its own challenges. You must decide which data sources to connect and how …

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Google is a Leader and positioned furthest in Vision and highest in Execution in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms

For the second consecutive year, Google has been named a Leader in the Gartner® Magic Quadrant™ for Conversational AI Platforms. Google received the furthest and highest in positioning on the “Vision” and “Execution” axes and is now ranked #1 in three out of four Critical Capabilities Use Cases. We believe this recognition reflects our continued …

The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add …