There are several reasons your fine-tuned model might not appear as available for inference in AWS Bedrock after training completes. First, confirm that the training job status is fully successful, not just marked as "completed." Training jobs can finish with errors (e.g., data validation failures) that prevent model deployment. Check the job's CloudWatch logs for warnings or errors, and verify that metrics like training loss or accuracy align with expectations. For example, if the loss plateaus abnormally, the model might not meet Bedrock’s internal validation criteria for deployment.
Second, ensure your AWS Identity and Access Management (IAM) role has the required permissions. The role used during training needs explicit access to Bedrock’s inference APIs. For instance, if the role lacks bedrock:GetFoundationModel or bedrock:InvokeModel permissions, the model won’t appear in the inference list. Additionally, verify that the model’s Amazon Resource Name (ARN) is correctly referenced in your code or console. A typo in the ARN (e.g., incorrect region or model ID) will prevent the model from being recognized.
Finally, Bedrock imposes regional restrictions and service quotas. If your training job ran in a region where Bedrock’s fine-tuning or inference features aren’t fully supported (e.g., certain regions lag in feature availability), the model won’t deploy. Check AWS documentation for supported regions. Additionally, if your account has a limit on the number of active models, you may need to delete unused models or request a quota increase. For example, if your account allows only five active models and you’ve already deployed five, the sixth won’t appear until you free up space. Wait 10–15 minutes after training completes, as deployment can take time even after the job finishes.