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Healthcare revolution: Vector databases for patient similarity search and precision diagnosis

Data Science Dojo

Traditional hea l t h c a r e databases struggle to grasp the complex relationships between patients and their clinical histories. Vector databases are revolutionizing healthcare data management. That’s where vector databases come in handy—they are made on purpose to handle this special kind of data.

Database 361
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Data mining

Dataconomy

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. Association rule mining Association rule mining identifies interesting relations between variables in large databases.

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning Blog

Vector database FloTorch selected Amazon OpenSearch Service as a vector database for its high-performance metrics. The implementation included a provisioned three-node sharded OpenSearch Service cluster. Amazon Bedrock APIs make it straightforward to use Amazon Titan Text Embeddings V2 for embedding data.

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Flipboard

The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. Its vector data store seamlessly integrates with operational data storage, eliminating the need for a separate database.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

It works by analyzing the visual content to find similar images in its database. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution. Display results : Display the top K similar results to the user. b64encode(resized_image).decode('utf-8')

AWS 117
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Use language embeddings for zero-shot classification and semantic search with Amazon Bedrock

AWS Machine Learning Blog

Caching is performed on Amazon CloudFront for certain topics to ease the database load. Amazon Aurora PostgreSQL-Compatible Edition and pgvector Amazon Aurora PostgreSQL-Compatible is used as the database, both for the functionality of the application itself and as a vector store using pgvector. Its hosted on AWS Lambda.

AWS 116
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OfferUp improved local results by 54% and relevance recall by 27% with multimodal search on Amazon Bedrock and Amazon OpenSearch Service

AWS Machine Learning Blog

These databases typically use k-nearest (k-NN) indexes built with advanced algorithms such as Hierarchical Navigable Small Worlds (HNSW) and Inverted File (IVF) systems. OpenSearch Service then uses the vectors to find the k-nearest neighbors (KNN) to the vectorized search term and image to retrieve the relevant listings.