Amazon Bedrock Model Customization

Complete guide to customizing Amazon Bedrock foundation models through fine-tuning, continued pre-training, reinforcement fine-tuning, and distillation.

Overview

Amazon Bedrock Model Customization allows you to adapt foundation models to your specific use cases without managing infrastructure. Four customization approaches are available:

1. Fine-Tuning (Supervised Learning)

Adapt models to specific tasks using labeled examples (input-output pairs). Best for:

• Task-specific optimization (classification, extraction, generation)
• Improving responses for domain terminology
• Teaching specific output formats
• Typical gains: 20-40% accuracy improvement