Introducing Model Distillation: Shrinking AI for Performance

In generative AI projects, a recurring challenge is maintaining high performance while simultaneously reducing costs and latency. This balancing act often proves difficult, prompting the need for innovative solutions.

What is a Foundation Model ?

Trained on massive datasets, foundation models (FMs) are large deep learning neural networks that have changed the way data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.

What is Model distillation ?

Model distillation is a technique in machine learning that aims to transfer knowledge from a larger, more complex model (the "teacher") to a smaller, more efficient model (the "student"). The goal is to create a compact model that approximates the performance of the larger model while requiring fewer computational resources.

This process typically involves training the student model to mimic the outputs or intermediate representations of the teacher model, rather than training it directly on the original dataset. By doing so, the student can learn to generalize in ways similar to the more powerful teacher model, often achieving comparable performance on specific tasks while being faster and more lightweight.

Model distillation is particularly useful in scenarios where deployment constraints (such as memory limitations or inference speed requirements) make using large models impractical. Model distillation creates efficient AI solutions by transferring knowledge from large to smaller models, enabling organizations to maximize their AI investments' value.

What is Amazon Bedrock ?

Amazon Bedrock is a fully managed service offering access to leading foundation models, enabling developers to build and scale generative AI applications with security, privacy, and responsible AI capabilities through a single API.

Model distillation in Amazon Bedrock:

With Amazon Bedrock Model Distillation, you can use smaller, faster, more cost-effective models that deliver use-case specific accuracy that is comparable to the most advanced models in Amazon Bedrock. Distilled models in Amazon Bedrock are up to 500% faster and up to 75% less expensive than original models, with less than 2% accuracy loss for use cases like RAG.

Why customize using Model Distillation ?

1/ Efficiency: Use-case specific accuracy of large models with speed of smaller one, 2/ Cost optimization: Reduced inference costs compared to larger, more advanced AI models, 3/ Advanced customization: Automated data synthesis and augmentation for optimized, use-case specific performance & 4/ Ease of use: Streamlined workflow automates response generation, data synthesis, and model fine-tuning.

Getting started:

Follow these steps to create your customized model using Amazon Bedrock's Model Distillation feature.

Interested to learn more ?

Join us live on Twitch.tv on June 10th, 2025 @ 2pm Pacific / 5pm Eastern to dive deep into Amazon Bedrock Model Distillation! We will cover live demo to help you get started with model distillation using Amazon Bedrock.

References:

Amazon Bedrock Model Distillation: Boost function calling accuracy while reducing cost and latency