Customize Amazon Nova models with Amazon Bedrock fine-tuning

This article explains how to customize Amazon Nova models using Amazon Bedrock's fine-tuning capabilities for domain-specific tasks.

• Amazon Bedrock supports three customization approaches: supervised fine-tuning (SFT), reinforcement fine-tuning (RFT), and model distillation
• Fine-tuning embeds knowledge directly into model weights, reducing inference costs and latency compared to prompt engineering or RAG
• Training data must be in JSONL format with labeled input-output examples; data quality is critical for success
• Key hyperparameters: epochCount (1-5), learningRateMultiplier, and learningRateWarmupSteps control training behavior
• Example: Nova Micro improved from 41.4% to 97% accuracy on airline intent classification with fine-tuning
• Training the ATIS example cost $2.18 and completed in 1.5 hours; on-demand inference uses same pricing as base model
• Monitor loss curves during training to detect convergence issues and adjust hyperparameters accordingly
• Fine-tuned models deploy via Amazon Bedrock on-demand inference without provisioned capacity requirements

Amazon Bedrock simplifies model customization through managed infrastructure, making fine-tuning accessible without deep ML expertise while delivering improved accuracy and reduced latency for domain-specific tasks.