If your fine-tuned model isn’t appearing as available for inference in AWS Bedrock after training completes, the most common reasons involve incomplete deployment steps, configuration mismatches, or permissions issues. Bedrock requires explicit steps to deploy a model for inference after training, and missing these can leave the model in a “trained but not deployed” state. Additionally, training jobs might succeed without automatically granting permissions to access the model, requiring manual adjustments to IAM roles or resource policies.
One key factor is the distinction between training and deployment workflows in Bedrock. For example, even if your training job finishes successfully, you might need to manually create a model endpoint or link the trained model artifact to an inference component. Bedrock often stores trained models in Amazon S3, but accessing them for inference requires configuring a dedicated endpoint or ensuring the model is registered in Bedrock’s model registry. Another common oversight is forgetting to specify the correct ARN (Amazon Resource Name) of the trained model when setting up the inference API. Without this, Bedrock won’t recognize the model as available. Permissions also play a role: your IAM role might have access to run training jobs but lack the bedrock:InvokeModel permission or policies allowing access to the S3 bucket where the model is stored.
To resolve this, start by verifying the training job’s output in the AWS Management Console or CLI. Check if the model artifact was saved to S3 and confirm its path matches your deployment configuration. Next, ensure you’ve explicitly deployed the model using Bedrock’s API (e.g., CreateModel or CreateEndpoint calls) or through the console. Review IAM policies to confirm your role has permissions for both training and inference actions. For instance, if your model is stored in s3://your-bucket/model-artifacts/, ensure the role includes s3:GetObject for that path. Finally, check for service limits—Bedrock might restrict concurrent deployments or require specific instance types for certain models. If all else fails, inspect CloudWatch logs for deployment errors, which often provide actionable clues like missing dependencies or incompatible model formats.