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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning - AI

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG).

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock allows you to build performant and customized Retrieval Augmented Generation (RAG) applications on top of AWS and third-party vector stores using both AWS and third-party models. RAG is a popular technique that combines the use of private data with large language models (LLMs).

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

As AI technology continues to evolve, the capabilities of generative AI agents are expected to expand, offering even more opportunities for customers to gain a competitive edge. These managed agents play conductor, orchestrating interactions between FMs, API integrations, user conversations, and knowledge sources loaded with your data.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning - AI

Clean data is important for good model performance. Data from social media, reviews, or any user generated contents can also contain toxic and biased contents, and you may need to filter them out using some pre-processing steps. Read and write access to an Amazon Simple Storage Service (Amazon S3) bucket. read HTML).

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Metadata Management: Process, Tools, Use Cases, and Best Practices

Altexsoft

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. Metadata storage usually implies developing a specialized repository. Passive metadata refers to basic technical characteristics and static metadata catalogs. Metadata storage.

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Why Reinvent the Wheel? The Challenges of DIY Open Source Analytics Platforms

Cloudera

In their effort to reduce their technology spend, some organizations that leverage open source projects for advanced analytics often consider either building and maintaining their own runtime with the required data processing engines or retaining older, now obsolete, versions of legacy Cloudera runtimes (CDH or HDP).