To monitor a fine-tuning job in Amazon Bedrock, you primarily use the AWS Management Console, Amazon CloudWatch, and AWS SDKs or CLI to track job status and access logs. The process is straightforward and designed to integrate with AWS’s existing monitoring tools, making it familiar for developers already using AWS services.
First, job status is visible in the Bedrock console under the Custom Models section. When you start a fine-tuning job, it appears in a list with statuses like InProgress, Completed, or Failed. For example, after submitting a job to fine-tune a Cohere model, you’ll see its current state, creation time, and base model type. If a job fails, the console often provides a brief error message to help diagnose issues. You can also use the AWS CLI with commands like aws bedrock get-model-customization-job --job-id <JOB_ID> to retrieve status details programmatically. This is useful for automation or integrating status checks into CI/CD pipelines.
Second, logs for fine-tuning jobs are stored in Amazon CloudWatch. Bedrock automatically streams logs to a CloudWatch log group named /aws/bedrock/model-customization-jobs. Within this group, logs are organized by job ID, allowing you to filter logs for specific training runs. For instance, you might check logs to see why a job stalled (e.g., out-of-memory errors) or to monitor metrics like training loss over time. You can access logs via the CloudWatch console, the AWS CLI (aws logs filter-log-events), or SDKs. Additionally, Bedrock can emit CloudWatch metrics like TrainingElapsedTime or TrainingSteps to track progress.
Finally, developers can use AWS SDKs (e.g., Boto3 for Python) to programmatically monitor jobs. For example, calling bedrock_client.get_model_customization_job(jobId=JOB_ID) returns detailed status and metadata. This is helpful for building custom dashboards or triggering alerts. If a job fails, combining console status messages with CloudWatch logs provides the most complete picture for troubleshooting. For instance, a “ResourceLimitExceeded” status might correlate with CloudWatch logs showing GPU memory exhaustion, guiding you to adjust training parameters like batch size.