Balancing Accuracy, Cost, and Latency in Video Semantic Search: Model Distillation Techniques on AWS
Introduction to Video Semantic Search Optimization
Overview of Model Distillation on Amazon Bedrock
Preparing Training Data for Effective Distillation
Running the Distillation Training Job
Deploying the Distilled Model for Enhanced Performance
Evaluating the Distilled Model Against Baseline Performance
Conclusion: Achieving Cost Efficiency Without Sacrificing Accuracy
About the Authors
Optimizing Video Semantic Search with Model Distillation on AWS
In the evolving realm of video semantic search, striking a balance between accuracy, latency, and cost is crucial. As we delve into the intricacies of optimizing video search models, we encounter a complex landscape. In the first part of our series, we laid the foundation for a multimodal video semantic search system on AWS, leveraging the Anthropic Claude Haiku model through Amazon Bedrock. While this model excels in accuracy regarding user search intent, it imposes a significant latency overhead, often elongating end-to-end search times to 2-4 seconds. This latency constitutes a staggering 75% of the overall query time.
Complex Routing Logic and Its Challenges
As the complexity of routing logic increases, so does the demand for processing power. Enterprises often deal with multifaceted metadata that goes beyond simple attributes like title, captions, and timestamps. Additional factors such as camera angles, mood and sentiment, and various domain-specific taxonomies add layers of complexity to the routing logic. Consequently, a nuanced prompt is necessary, which leads to slower and more expensive responses.
However, there’s a silver lining: model customization. Rather than being constrained to fast but overly simplistic models or larger, slower, yet more accurate models, there’s a path to achieve all three goals: speed, accuracy, and cost-effectiveness. This is where the technique of model distillation shines.
The Power of Model Distillation
In this article, we’ll explore how to implement Model Distillation within Amazon Bedrock, effectively transferring routing intelligence from a robust teacher model (Amazon Nova Premier) to a more nimble student model (Amazon Nova Micro). This innovative approach can reduce inference costs by over 95% while halving latency, all without sacrificing the sophisticated routing quality required for effective video search.
Solution Overview
To guide you through the full distillation pipeline, we’ve structured a Jupyter notebook containing the following key steps:
- Prepare Training Data: Generate 10,000 synthetic labeled examples leveraging Nova Premier and upload them in Bedrock’s distillation format to Amazon Simple Storage Service (Amazon S3).
- Run Distillation Training Job: Configure the job with identifiers for both the teacher and student models and submit via Amazon Bedrock.
- Deploy the Distilled Model: Employ on-demand inference for flexible, pay-per-use access to the custom model.
- Evaluate the Distilled Model: Assess the routing quality against both the base Nova Micro and the original Claude Haiku baseline using Amazon Bedrock’s evaluation tools.
The complete notebook, along with the training data generation script and evaluation utilities, is available in our GitHub repository.
Preparing Training Data
One of the primary advantages of model distillation over other customization techniques, like supervised fine-tuning (SFT), lies in its flexibility. While SFT requires every training example to have a fully labeled response, distillation only requires prompts. Amazon Bedrock automatically generates high-quality responses from the teacher model, employing data synthesis and augmentation techniques to create a diverse training dataset of up to 15,000 prompt-response pairs.
By creating 10,000 synthetic labeled examples with a balanced distribution across visual, audio, transcription, and metadata queries, we ensure that our training data captures a wide range of expected search inputs while preventing overfitting. If further examples are required, the provided generate_training_data.py script can synthesize more tailored training data.
Figure 1: Weight distribution across the 10,000 training examples.
Running the Distillation Training Job
Once the training data is in place on Amazon S3, we can trigger the distillation job. This involves using the prompts to generate responses from the teacher model, which then serve as the training signal for the student model. With Amazon Bedrock managing the training orchestration, we can focus on specifying model identifiers and IAM roles.
Example Job Submission Code:
import boto3
from datetime import datetime
bedrock_client = boto3.client(service_name="bedrock")
teacher_model = "us.amazon.nova-premier-v1:0"
student_model = "amazon.nova-micro-v1:0:128k"
job_name = f"video-search-distillation-{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}"
model_name = "nova-micro-video-router-v1"
response = bedrock_client.create_model_customization_job(
jobName=job_name,
customModelName=model_name,
roleArn=distillation_role_arn,
baseModelIdentifier=student_model,
customizationType="DISTILLATION",
trainingDataConfig={"s3Uri": training_s3_uri},
outputDataConfig={"s3Uri": output_s3_uri},
customizationConfig={
"distillationConfig": {
"teacherModelConfig": {
"teacherModelIdentifier": teacher_model,
"maxResponseLengthForInference": 1000
}
}
}
)
job_arn = response['jobArn']Deploying the Distilled Model
After the distillation job is complete, the custom model becomes available in your Amazon Bedrock account, ready for deployment. Teams can choose between two deployment options:
- Provisioned Throughput: For predictable, high-volume workloads.
- On-Demand Inference: For flexible, pay-per-use access with no upfront commitment.
The following snippet illustrates how to deploy the distilled model through on-demand inference:
import uuid
deployment_name = f"nova-micro-video-router-{datetime.now().strftime('%Y-%m-%d')}"
response = bedrock_client.create_custom_model_deployment(
modelDeploymentName=deployment_name,
modelArn=custom_model_arn,
description="Distilled Nova Micro for video search modality weight prediction (4 weights)",
tags=[
{"key": "UseCase", "value": "VideoSearch"},
{"key": "Version", "value": "v2-4weights"},
],
clientRequestToken=f"deployment-{uuid.uuid4()}",
)
deployment_arn = response['modelDeploymentArn']Evaluating the Distilled Model
To validate that our distillation process improved the routing capabilities, we compared outputs from both the distilled Nova Micro and the original Nova Micro. The distilled model consistently produced well-formed JSON outputs with accurate, numeric weights.
Figure 2: Model performance comparison (Distilled Nova Micro vs. Claude 4.5 Haiku).
The distilled model achieved an impressive LLM-as-judge score of 4.0 out of 5, matching the results of the more complex Claude 4.5 Haiku model while maintaining a significantly lower latency of 833ms compared to 1,741ms for the complex model.
Conclusion
This post is part two of our series that builds on the foundations laid out in Part 1. This exploration into model distillation addresses real-world challenges in balancing speed, accuracy, and cost in video semantic search systems. By distilling the routing intelligence of Amazon Nova Premier into a compact Nova Micro model, we have significantly minimized latency and costs without losing the critical routing finesse required for effective video search.
If you’re looking to optimize multimodal video search at scale, model distillation presents an efficient pathway to achieving production-grade performance while maintaining optimal search accuracy. For the complete implementation, check out our GitHub repository and start building your own customized solution today!
About the Authors
Amit Kalawat
A Principal Solutions Architect at AWS, Amit helps enterprise customers transform their businesses and transition to the cloud.
James Wu
James is a Principal GenAI/ML Specialist Solutions Architect at AWS, specializing in generative AI. His background encompasses over a decade of experience in architecture and development.
Bimal Gajjar
Bimal is a Senior Solutions Architect at AWS, focusing on scalable cloud storage and data solutions. With over 25 years of expertise, he collaborates with Global Accounts for effective cloud deployments.