Gemini in Google Sheets just achieved state-of-the-art performance.
Today we announced new beta features for Gemini in Sheets to help you create, organize and edit entire sheets, from basic tasks to complex data analysis — just describe …
Run NVIDIA Nemotron 3 Nano as a fully managed serverless model on Amazon Bedrock
This post is cowritten with Abdullahi Olaoye, Curtice Lockhart, Nirmal Kumar Juluru from NVIDIA. We are excited to announce that NVIDIA’s Nemotron 3 Nano is now available as a fully managed and serverless model in Amazon Bedrock. This follows our earlier announcement at AWS re:Invent supporting NVIDIA Nemotron 2 Nano 9B and NVIDIA Nemotron 2 …
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Access Anthropic Claude models in India on Amazon Bedrock with Global cross-Region inference
The adoption and implementation of generative AI inference has increased with organizations building more operational workloads that use AI capabilities in production at scale. To help customers achieve the scale of their generative AI applications, Amazon Bedrock offers cross-Region inference (CRIS) profiles. CRIS is a powerful feature that organizations can use to seamlessly distribute inference …
How our open-source AI model SpeciesNet is helping to promote wildlife conservation
An overview of SpeciesNet, our open-source AI model that is helping people around the world protect and conserve wildlife.
Ask a Techspert: How does AI understand my visual searches?
Learn more about AI Mode in Search’s query fan-out method for visual search.
The latest AI news we announced in February
Here are Google’s latest AI updates from February 2026
Drive organizational growth with Amazon Lex multi-developer CI/CD pipeline
As your conversational AI initiatives evolve, developing Amazon Lex assistants becomes increasingly complex. Multiple developers working on the same shared Lex instance leads to configuration conflicts, overwritten changes, and slower iteration cycles. Scaling Amazon Lex development requires isolated environments, version control, and automated deployment pipelines. By adopting well-structured continuous integration and continuous delivery (CI/CD) practices, …
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Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints
Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain greater control over their deployments, optimize costs, and align with compliance requirements. However, this flexibility introduces a critical technical challenge: response format incompatibility with Strands agents. While these custom …
Embed Amazon Quick Suite chat agents in enterprise applications
Organizations can face two critical challenges with conversational AI. First, users need answers where they work—in their CRM, support console, or analytics portal—not in separate tools. Second, implementing a secure embedded chat in their applications can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. Amazon Quick Suite embedded …
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Unlock powerful call center analytics with Amazon Nova foundation models
Call center analytics play a crucial role in improving customer experience and operational efficiency. With foundation models (FMs), you can improve the quality and efficiency of call center operations and analytics. Organizations can use generative AI to assist human customer support agents and managers of contact center teams, so they can gain insights that are …
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