<ul><li>Vector databases are crucial for modern AI apps like recommendation systems and language models.</li><li>This article focuses on building an AWS scalable vector database efficiently.</li><li>Vector databases store high-dimensional embeddings for unstructured data like text, images, and audio.</li><li>Vector search uses techniques like HNSW, FAISS, and Annoy to find similar items efficiently.</li><li>Challenges in scaling vector databases include cost, indexing performance, storage, and query latency.</li><li>Using AWS services like OpenSearch, Aurora, and DynamoDB can help in building scalable vector search pipelines.</li><li>Creating vector embeddings from AI models like BERT and indexing them is essential for vector databases.</li><li>Optimizing indexing parameters, using parallelism, and optimizing costs are key considerations for performance.</li><li>AWS Lambda, Amazon SageMaker, and Graviton-powered EC2 instances can aid in optimizing costs.</li><li>Amazon OpenSearch, Aurora with pgvector, and DynamoDB are recommended for AI-driven apps on AWS.</li></ul>

AI Database Creation with an AWS Scalable Vector Database

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