Using Amazon S3 Vectors (preview) to semantically search using a media lake on AWS
Media Blog
The article discusses AWS's new S3 Vectors feature and how media organizations can leverage semantic search for content discovery using a media lake architecture.
- S3 Vectors enables cost-effective vector storage and querying of media assets at massive scale
- Uses TwelveLabs Marengo multimodal embedding model on Amazon Bedrock for generating vector embeddings
- Provides a serverless architecture for semantic search across images, video, and audio content
- Enables natural language queries to find relevant media assets with sub-second performance
- Reduces content search time from hours to minutes while maintaining scalability
The solution allows media organizations to efficiently discover and utilize their digital libraries through AI-powered semantic search capabilities.
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