Monitoring Progress Across Iterations - Amazon SageMaker AI
Create an MLflow appAccess the MLflow appKey metrics to trackDetermining when to stop
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Monitoring Progress Across Iterations

You can track metrics via MLflow.

Create an MLflow app

Using Studio UI: If you create a training job through the Studio UI, a default MLflow app is created automatically and selected by default under Advanced Options.

Using CLI: If you use the CLI, you must create an MLflow app and pass it as an input to the training job API request.

mlflow_app_name="<enter your MLflow app name>"
role_arn="<enter your role ARN>" 
bucket_name="<enter your bucket name>" 
region="<enter your region>"

mlflow_app_arn=$(aws sagemaker create-mlflow-app \
  --name $mlflow_app_name \
  --artifact-store-uri "s3://$bucket_name" \
  --role-arn $role_arn \
  --region $region)

Access the MLflow app

Using CLI: Create a pre-signed URL to access the MLflow app UI:

aws sagemaker create-presigned-mlflow-app-url \
  --arn $mlflow_app_arn \
  --region $region \
  --output text

Using Studio UI: The Studio UI displays key metrics stored in MLflow and provides a link to the MLflow app UI.

Key metrics to track

Monitor these metrics across iterations to assess improvement and track the job progress:

For SFT

  • Training loss curves

  • Number of samples consumed and time to process samples

  • Performance accuracy on held-out test sets

  • Format compliance (e.g., valid JSON output rate)

  • Perplexity on domain-specific evaluation data

For RFT

  • Average reward scores over training

  • Reward distribution (percentage of high-reward responses)

  • Validation reward trends (watch for over-fitting)

  • Task-specific success rates (e.g., code execution pass rate, math problem accuracy)

General

  • Benchmark performance deltas between iterations

  • Human evaluation scores on representative samples

  • Production metrics (if deploying iteratively)

Determining when to stop

Stop iterating when:

  • Performance plateaus: Additional training no longer meaningfully improves target metrics

  • Technique switching helps: If one technique plateaus, try switching (e.g., SFT → RFT → SFT) to break through performance ceilings

  • Target metrics achieved: Your success criteria are met

  • Regression detected: New iterations degrade performance (see rollback procedures below)

For detailed evaluation procedures, refer to the Evaluation section.