To troubleshoot a failed or incomplete fine-tuning job in AWS Bedrock, start by examining logs and error messages. Bedrock integrates with AWS CloudWatch, so check the CloudWatch log group associated with your fine-tuning job. Look for explicit error messages, such as permission issues, resource limits, or data validation failures. For example, a common error might indicate insufficient permissions for Bedrock to access your training data in an S3 bucket. Verify that the IAM role attached to the job has the required s3:GetObject permissions and that the S3 bucket policy allows access. If the logs are unclear, use the AWS CLI or SDK to fetch the job status with bedrock get-model-customization-job --job-id <ID> to retrieve structured details about the failure.
Next, validate your training data and configuration. Ensure your dataset is formatted correctly (e.g., JSONL for classification tasks) and meets Bedrock’s requirements, such as file size limits or token counts. For instance, if you’re fine-tuning a text generation model, confirm that prompts and completions are structured properly. Test a small subset of your data locally using a script to catch formatting issues before re-submitting the job. Additionally, review hyperparameters like learningRate or batchSize—values outside Bedrock’s supported ranges (e.g., a learning rate of 0 or excessively large batch sizes) can cause failures. Compare your settings against Bedrock’s documentation for the specific base model you’re using.
Finally, check service quotas and regional availability. AWS imposes limits on concurrent fine-tuning jobs per account or region. Use the AWS Service Quotas console to verify you haven’t exceeded limits like bedrock.custom-model.count or bedrock.training-jobs-per-region. If the job stalls without errors, consider regional service outages by checking the AWS Health Dashboard. For persistent issues, enable AWS Bedrock’s debug mode (if available) for deeper insights or contact AWS Support with the job ID, logs, and a minimal reproducible example of your dataset and configuration to expedite resolution.