Bringing the power of Personal Intelligence to more people
We’re expanding Personal Intelligence across AI Mode in Search, the Gemini app and Gemini in Chrome.
Our latest investment in open source security for the AI era
Google is making new investments, building new tools and developing code security to improve open source security.
AWS AI League: Atos fine-tunes approach to AI education
This post is co-written with Mark Ross from Atos. Organizations pursuing AI transformation can face a familiar challenge: how to upskill their workforce at scale in a way that changes …
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AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production
AI is moving fast, and for most of our customers, the real opportunity isn’t in experimenting with it—it’s in running AI in production where it drives meaningful business outcomes. This …
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Agentic AI in the Enterprise Part 2: Guidance by Persona
This is Part II of a two-part series from the AWS Generative AI Innovation Center. If you missed Part I, refer to Operationalizing Agentic AI Part 1: A Stakeholder’s Guide. …
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Introducing Disaggregated Inference on AWS powered by llm-d
We thank Greg Pereira and Robert Shaw from the llm-d team for their support in bringing llm-d to AWS. In the agentic and reasoning era, large language models (LLMs) generate …
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How Workhuman built multi-tenant self-service reporting using Amazon Quick Sight embedded dashboards
This post is cowritten with Ilija Subanovic and Michael Rice from Workhuman. Workhuman’s customer service and analytics team were drowning in one-time reporting requests from seven million users worldwide—a common …
Build an offline feature store using Amazon SageMaker Unified Studio and SageMaker Catalog
Building and managing machine learning (ML) features at scale is one of the most critical and complex challenges in modern data science workflows. Organizations often struggle with fragmented feature pipelines, …
P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM
EAGLE is the state-of-the-art method for speculative decoding in large language model (LLM) inference, but its autoregressive drafting creates a hidden bottleneck: the more tokens that you speculate, the more …
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Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption
As organizations scale their generative AI workloads on Amazon Bedrock, operational visibility into inference performance and resource consumption becomes critical. Teams running latency-sensitive applications must understand how quickly models begin …