Multimodal embeddings at scale: AI data lake for media and entertainment workloads
Amazon AWS AI Blog / 3/13/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical UsageModels & Research
Key Points
- It demonstrates how to build a scalable multimodal video search system enabling natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service.
- It moves beyond manual tagging and keyword-based searches to semantic search that captures the full richness of video content.
- It covers architectural and operational considerations for deploying the system at scale, including data lake organization, embeddings, indexing, and retrieval with AWS services.
- It targets media and entertainment workloads, illustrating practical steps to improve search relevance and asset discovery.
This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the full richness of video content.
Related Articles
Manus、AIエージェントをデスクトップ化 ローカルPC上でファイルやアプリを直接操作可能にのサムネイル画像
Ledge.ai
The programming passion is melting
Dev.to
Best AI Tools for Property Managers in 2026
Dev.to
Building “The Sentinel” – AI Parametric Insurance at Guidewire DEVTrails
Dev.to
Maximize Developer Revenue with Monetzly's Innovative API for AI Conversations
Dev.to
