AWS has introduced a robust architecture for managing "multimodal embeddings," which essentially allow computers to understand video content by combining both visual and audio data into a single numerical representation. By leveraging the new Amazon Nova model suite, developers can now index massive media libraries without relying on manual tagging or basic keyword searches. This transition to "semantic search"—finding content based on meaning rather than just exact words—is demonstrated through a massive experiment involving nearly 800,000 videos.

The technical pipeline utilizes Amazon Nova Multimodal Embeddings to break videos into 15-second segments, capturing scene changes while maintaining efficient storage. Interestingly, the researchers found that using 1024-dimensional embeddings (a specific size of numerical data) offered a 3x cost saving over larger formats with almost no loss in search accuracy. For even greater precision, the system uses a "hybrid search" approach, which blends vector similarity (mathematical closeness of concepts) with traditional keyword matching.

Processing this volume of data—over 8,000 hours of footage—was completed in just 41 hours at a cost of approximately $27,000 for the first year. This milestone demonstrates that industrial-scale AI data lakes are becoming financially viable for media and entertainment companies. By using Amazon Nova Lite for descriptive tagging and OpenSearch for indexing, organizations can now implement "video-to-video" discovery, where the system identifies similar clips based on visual context rather than metadata.

AWS has introduced a robust architecture for managing "multimodal embeddings," which essentially allow computers to understand video content by combining both visual and audio data into a single numerical representation. By leveraging the new Amazon Nova model suite, developers can now index massive media libraries without relying on manual tagging or basic keyword searches. This transition to "semantic search"—finding content based on meaning rather than just exact words—is demonstrated through a massive experiment involving nearly 800,000 videos.

The technical pipeline utilizes Amazon Nova Multimodal Embeddings to break videos into 15-second segments, capturing scene changes while maintaining efficient storage. Interestingly, the researchers found that using 1024-dimensional embeddings (a specific size of numerical data) offered a 3x cost saving over larger formats with almost no loss in search accuracy. For even greater precision, the system uses a "hybrid search" approach, which blends vector similarity (mathematical closeness of concepts) with traditional keyword matching.

Processing this volume of data—over 8,000 hours of footage—was completed in just 41 hours at a cost of approximately $27,000 for the first year. This milestone demonstrates that industrial-scale AI data lakes are becoming financially viable for media and entertainment companies. By using Amazon Nova Lite for descriptive tagging and OpenSearch for indexing, organizations can now implement "video-to-video" discovery, where the system identifies similar clips based on visual context rather than metadata.