According to aws.amazon.com, AWS has published guidance on using Model Distillation on Amazon Bedrock to optimize video semantic search systems, achieving significant performance improvements while maintaining accuracy.
The approach addresses a key challenge in video semantic search: balancing accuracy, cost, and latency. According to the source, faster, smaller models lack routing intelligence, while larger, accurate models add significant latency overhead. In a previous implementation using the Anthropic Claude Haiku model for intent routing, the model contributed to 75% of overall latency, pushing end-to-end search time to 2-4 seconds.
According to aws.amazon.com, the Model Distillation technique transfers routing intelligence from a large teacher model (Amazon Nova Premier) into a smaller student model (Amazon Nova Micro). This approach “cuts inference cost by over 95% and reduces latency by 50% while maintaining the nuanced routing quality that the task demands,” the source states.
The solution builds on Amazon Nova Multimodal Embeddings, which aws.amazon.com describes as “a unified embedding model that natively processes text, documents, images, video, and audio into a shared semantic vector space.” According to the source, traditional approaches convert all video signals into text through transcription or tagging, which “inevitably loses critical information” including temporal understanding.
The training process uses 10,000 synthetic labels, according to aws.amazon.com, and is demonstrated in a Jupyter notebook that walks through the full distillation pipeline.