Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock

This article demonstrates using Amazon Bedrock Model Distillation to optimize video semantic search by transferring routing intelligence from a large teacher model (Nova Premier) to a smaller student model (Nova Micro).

• Model distillation reduces inference cost by over 95% and latency by 50%
• Prepare 10,000 synthetic training examples; Bedrock auto-generates responses from teacher model
• Submit distillation job specifying teacher/student models and training data location
• Deploy distilled model using on-demand inference with pay-per-use pricing
• Distilled Nova Micro achieves 4.0/5 LLM-as-judge score, matching Claude Haiku quality
• Mean latency reduced from 1,741ms to 833ms; token costs drop from $0.80-$5.00 to $0.000035-$0.000140 per 1K
• Complete implementation available in GitHub repository with notebook and evaluation utilities

Model distillation enables production-grade cost efficiency for multimodal video search without sacrificing accuracy or routing quality.