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Innovating at speed: BMW’s generative AI solution for cloud incident analysis

AWS Machine Learning Blog

In this post, we explain how BMW uses generative AI technology on AWS to help run these digital services with high availability. Or was the database password for the central subscription service rotated again? It requires checking many systems and teams, many of which might be failing, because theyre interdependent.

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Unbundling the Graph in GraphRAG

O'Reilly Media

One popular term encountered in generative AI practice is retrieval-augmented generation (RAG). Store these chunks in a vector database, indexed by their embedding vectors. The various flavors of RAG borrow from recommender systems practices, such as the use of vector databases and embeddings.

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Build a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

AI agents continue to gain momentum, as businesses use the power of generative AI to reinvent customer experiences and automate complex workflows. In this post, we explore how to build an application using Amazon Bedrock inline agents, demonstrating how a single AI assistant can adapt its capabilities dynamically based on user roles.

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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

Solution overview For our custom multimodal chat assistant, we start by creating a vector database of relevant text documents that will be used to answer user queries. About the Authors Emmett Goodman is an Applied Scientist at the Amazon Generative AI Innovation Center.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

The intersection of AI and financial analysis presents a compelling opportunity to transform how investment professionals access and use credit intelligence, leading to more efficient decision-making processes and better risk management outcomes. These operational inefficiencies meant that we had to revisit our solution architecture.

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How Q4 Inc. used Amazon Bedrock, RAG, and SQLDatabaseChain to address numerical and structured dataset challenges building their Q&A chatbot

Flipboard

needed to address some of these challenges in one of their many AI use cases built on AWS. During the embeddings experiment, the dataset was converted into embeddings, stored in a vector database, and then matched with the embeddings of the question to extract context. Based on the initial tests, this method showed great results.

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Build multi-agent systems with LangGraph and Amazon Bedrock

AWS Machine Learning Blog

Beyond simple language understanding, real-world applications require managing complex workflows, connecting to external data, and coordinating multiple AI capabilities. In these real-world scenarios, agents can be a game changer, delivering more customized generative AI applications. Flight search needed for March 15, 2025.

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