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Category: Machine learning

March 19, 2025Machine learning

Amazon Q Business now available in Europe (Ireland) AWS Region

Today, we are excited to announce that Amazon Q Business—a fully managed […]

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March 18, 2025Machine learning

NeMo Retriever Llama 3.2 text embedding and reranking NVIDIA NIM microservices now available in Amazon SageMaker JumpStart

Today, we are excited to announce that the NeMo Retriever Llama3.2 Text […]

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March 18, 2025Machine learning

Running NVIDIA NeMo 2.0 Framework on Amazon SageMaker HyperPod

This post is cowritten with Abdullahi Olaoye, Akshit Arora and Eliuth Triana […]

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March 18, 2025Machine learning

Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

SQL is one of the key languages widely used across businesses, and […]

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March 18, 2025Machine learning

Amazon Bedrock Guardrails announces IAM Policy-based enforcement to deliver safe AI interactions

As generative AI adoption accelerates across enterprises, maintaining safe, responsible, and compliant […]

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March 18, 2025Machine learning

Revolutionizing clinical trials with the power of voice and AI

In the rapidly evolving healthcare landscape, patients often find themselves navigating a […]

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March 18, 2025Machine learning

Unleash AI innovation with Amazon SageMaker HyperPod

The rise of generative AI has significantly increased the complexity of building, […]

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March 17, 2025Machine learning

Intelligent healthcare assistants: Empowering stakeholders with personalized support and data-driven insights

Large language models (LLMs) have revolutionized the field of natural language processing, […]

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March 14, 2025Machine learning

Getting started with computer use in Amazon Bedrock Agents

Computer use is a breakthrough capability from Anthropic that allows foundation models […]

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March 14, 2025Machine learning

Evaluating RAG applications with Amazon Bedrock knowledge base evaluation

Organizations building and deploying AI applications, particularly those using large language models […]

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