Customize Amazon Nova models with Amazon Bedrock fine-tuning
Amazon AWS AI Blog / 4/9/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage
Key Points
- The article provides a step-by-step implementation guide to fine-tune Amazon Bedrock Amazon Nova models using an intent classifier example for a domain-specific task.
- It emphasizes building high-quality training data as the main driver of meaningful performance improvements.
- The guide covers how to configure hyperparameters to optimize learning while avoiding overfitting.
- It explains how to deploy the resulting fine-tuned model and measure outcomes such as improved accuracy and reduced latency.
- It also describes evaluation using training metrics and loss curves to validate whether the fine-tuning worked effectively.
In this post, we'll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you'll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We'll show you how to evaluate your results using training metrics and loss curves.
