AI

How Amazon uses Amazon Nova models to automate operational readiness testing for new fulfillment centers

Amazon is a global ecommerce and technology company that operates a vast network of fulfillment centers to store, process, and ship products to customers worldwide. The Amazon Global Engineering Services (GES) team is responsible for facilitating operational readiness across the company’s rapidly expanding network of fulfillment centers. When launching new fulfillment centers, Amazon must verify

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Iberdrola enhances IT operations using Amazon Bedrock AgentCore

Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. By using different agentic architectures, Iberdrola has transformed the way thousands of change requests and incident tickets are managed, streamlining processes and enhancing productivity across departments. Through its partnership with AWS, Iberdrola implemented those agents

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Building real-time voice assistants with Amazon Nova Sonic compared to cascading architectures

Voice AI agents are reshaping how we interact with technology. From customer service and healthcare assistance to home automation and personal productivity, these intelligent virtual assistants are rapidly gaining popularity across industries. Their natural language capabilities, constant availability, and increasing sophistication make them valuable tools for businesses seeking efficiency and individuals desiring seamless digital experiences.

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Automated Reasoning checks rewriting chatbot reference implementation

Today, we are publishing a new open source sample chatbot that shows how to use feedback from Automated Reasoning checks to iterate on the generated content, ask clarifying questions, and prove the correctness of an answer. The chatbot implementation also produces an audit log that includes mathematically verifiable explanations for the answer validity and a

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Scale LLM fine-tuning with Hugging Face and Amazon SageMaker AI

Enterprises are increasingly shifting from relying solely on large, general-purpose language models to developing specialized large language models (LLMs) fine-tuned on their own proprietary data. Although foundation models (FMs) offer impressive general capabilities, they often fall short when applied to the complexities of enterprise environments—where accuracy, security, compliance, and domain-specific knowledge are non-negotiable. To meet

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