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Unstructured data management and governance using AWS AI/ML and analytics services

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However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data. Amazon Rekognition – This image and video analysis service uses ML to extract metadata from visual data.

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Copyright-Aware AI: Let’s Make It So

O'Reilly Media

On April 22, 2022, I received an out-of-the-blue text from Sam Altman inquiring about the possibility of training GPT-4 on OReilly books. And now, of course, given reports that Meta has trained Llama on LibGen, the Russian database of pirated books, one has to wonder whether OpenAI has done the same. He never did.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning Blog

You marked your calendars, you booked your hotel, and you even purchased the airfare. Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. To help you plan your agenda for this year’s re:Invent, here are some highlights of the generative AI and ML track.

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Enhanced diagnostics flow with LLM and Amazon Bedrock agent integration

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To meet the feature requirements, the system operation process includes the following steps: Charging data is processed through the EV service before entering the database. The charging history data and pricing data are stored in the EV database. Amazon EventBridge Scheduler periodically triggers the EV service to perform analysis.

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Architect a mature generative AI foundation on AWS

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A generative AI foundation can provide primitives such as models, vector databases, and guardrails as a service and higher-level services for defining AI workflows, agents and multi-agents, tools, and also a catalog to encourage reuse. Considerations here are choice of vector database, optimizing indexing pipelines, and retrieval strategies.

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Protect sensitive data in RAG applications with Amazon Bedrock

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The following diagram illustrates how RBAC works with metadata filtering in the vector database. Amazon Bedrock Knowledge Bases performs similarity searches on the OpenSearch Service vector database and retrieves relevant chunks (optionally, you can improve the relevance of query responses using a reranker model in the knowledge base).

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

AWS Machine Learning Blog

Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The resulting vectors are stored in OpenSearch Service databases for efficient retrieval and querying.

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