To fine-tune a model in Amazon Bedrock with your dataset, follow these steps:
1. Prepare Your Dataset and Configure Access
First, structure your dataset in JSON Lines (JSONL) format, where each line contains a prompt-completion pair. For example:
{"prompt": "Translate 'hello' to French", "completion": "bonjour"}
Ensure the data is clean, relevant, and split into training and validation sets. Upload the files to an Amazon S3 bucket. Configure bucket permissions to grant Bedrock access via IAM roles or bucket policies. Verify that the S3 URI paths are correct, as Bedrock will use them to locate the data.
2. Launch the Fine-Tuning Job In the Bedrock console, select the base model (e.g., Amazon Titan, Anthropic Claude) that supports customization. Not all models in Bedrock allow fine-tuning, so check documentation for compatibility. Specify the S3 paths for training and validation data, then configure hyperparameters like epochs or batch size (if adjustable). Some models may handle hyperparameters automatically. Submit the job via the console, CLI, or SDK. Bedrock manages the infrastructure, so you don’t provision servers. Monitor the job’s progress in the console, which typically shows metrics like training loss or validation accuracy.
3. Deploy and Test the Custom Model Once training completes, Bedrock saves the fine-tuned model to your S3 bucket. Deploy it as a dedicated endpoint or use it on-demand. Test the model by invoking it via the Bedrock API with sample inputs to validate performance. Track costs, as fine-tuning and inference for custom models incur charges based on usage. Iterate by adjusting the dataset or parameters if results are suboptimal.
Key Considerations
- Data Quality: Poorly formatted or irrelevant data will degrade performance.
- Costs: Fine-tuning costs depend on model size and training duration. Validate business needs before large-scale jobs.
- Permissions: Ensure Bedrock’s IAM role has read access to the S3 bucket and write access for output.
- Limitations: Customization might not support drastic architectural changes—focus on task-specific tuning rather than overhauling the model.
This process allows you to adapt foundational models to domain-specific tasks (e.g., medical text analysis or legal document summarization) while leveraging AWS’s managed infrastructure.