Remove Generative AI Remove IDP Remove LLM
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Secure a generative AI assistant with OWASP Top 10 mitigation

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A common use case with generative AI that we usually see customers evaluate for a production use case is a generative AI-powered assistant. If there are security risks that cant be clearly identified, then they cant be addressed, and that can halt the production deployment of the generative AI application.

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Unleashing the multimodal power of Amazon Bedrock Data Automation to transform unstructured data into actionable insights

AWS Machine Learning Blog

Gartner predicts that by 2027, 40% of generative AI solutions will be multimodal (text, image, audio and video) by 2027, up from 1% in 2023. The McKinsey 2023 State of AI Report identifies data management as a major obstacle to AI adoption and scaling. billion in 2025 to USD 66.68 billion by 2032 with a CAGR of 30.1 %.

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

The landscape of enterprise application development is undergoing a seismic shift with the advent of generative AI. Agent Creator is a no-code visual tool that empowers business users and application developers to create sophisticated large language model (LLM) powered applications and agents without programming expertise.

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Streamline financial workflows with generative AI for email automation

AWS Machine Learning Blog

This represents a major opportunity for businesses to optimize this workflow, save time and money, and improve accuracy by modernizing antiquated manual document handling with intelligent document processing (IDP) on AWS. This post explores how generative AI can make working with business documents and email attachments more straightforward.

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Build private and secure enterprise generative AI applications with Amazon Q Business using IAM Federation

AWS Machine Learning Blog

Amazon Q Business is a conversational assistant powered by generative artificial intelligence (AI) that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems, which each user is authorized to access.

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Effectively use prompt caching on Amazon Bedrock

AWS Machine Learning Blog

How prompt caching works Large language model (LLM) processing is made up of two primary stages: input token processing and output token generation. As you send more requests with the same prompt prefix, marked by the cache checkpoint, the LLM will check if the prompt prefix is already stored in the cache.

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Intelligent document processing with Amazon Textract, Amazon Bedrock, and LangChain

AWS Machine Learning Blog

Document processing has witnessed significant advancements with the advent of Intelligent Document Processing (IDP). With IDP, businesses can transform unstructured data from various document types into structured, actionable insights, dramatically enhancing efficiency and reducing manual efforts.

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