Create a Model Quality Baseline - Amazon SageMaker

Create a Model Quality Baseline

Create a baseline job that compares your model predictions with ground truth labels in a baseline dataset that you have stored in Amazon S3. Typically, you use a training dataset as the baseline dataset. The baseline job calculates metrics for the model and suggests constraints to use to monitor model quality drift.

To create a baseline job, you need to have a dataset that contains predictions from your model along with labels that represent the ground truth for your data.

To create a baseline job use the ModelQualityMonitor class provided by the SageMaker Python SDK, and complete the following steps.

To create a model quality baseline job

  1. First, create an instance of the ModelQualityMonitor class. The following code snippet shows how to do this.

    from sagemaker import get_execution_role, session, Session from sagemaker.model_monitor import ModelQualityMonitor role = get_execution_role() session = Session() model_quality_monitor = ModelQualityMonitor( role=role, instance_count=1, instance_type='ml.m5.xlarge', volume_size_in_gb=20, max_runtime_in_seconds=1800, sagemaker_session=session )
  2. Now call the suggest_baseline method of the ModelQualityMonitor object to run a baseline job. The following code snippet assumes that you have a baseline dataset that contains both predictions and labels stored in Amazon S3.

    baseline_job_name = "MyBaseLineJob" job = model_quality_monitor.suggest_baseline( job_name=baseline_job_name, baseline_dataset=baseline_dataset_uri, # The S3 location of the validation dataset. dataset_format=DatasetFormat.csv(header=True), output_s3_uri = baseline_results_uri, # The S3 location to store the results. problem_type='BinaryClassification', inference_attribute= "prediction", # The column in the dataset that contains predictions. probability_attribute= "probability", # The column in the dataset that contains probabilities. ground_truth_attribute= "label" # The column in the dataset that contains ground truth labels. ) job.wait(logs=False)
  3. After the baseline job finishes, you can see the constraints that the job generated. First, get the results of the baseline job by calling the latest_baselining_job method of the ModelQualityMonitor object.

    baseline_job = model_quality_monitor.latest_baselining_job
  4. The baseline job suggests constraints, which are thresholds for metrics that model monitor measures. If a metric goes beyond the suggested threshold, Model Monitor reports a violation. To view the constraints that the baseline job generated, call the suggested_constraints method of the baseline job. The following code snippet loads the constraints for a binary classification model into a Pandas dataframe.

    import pandas as pd pd.DataFrame(baseline_job.suggested_constraints().body_dict["binary_classification_constraints"]).T

    We recommend that you view the generated constraints and modify them as necessary before using them for monitoring. For example, if a constraint is too aggressive, you might get more alerts for violations than you want.

  5. When you are satisfied with the constraints, pass them as the constraints parameter when you create a monitoring schedule. For more information, see Schedule Model Quality Monitoring Jobs.

The suggested baseline constraints are contained in the constraints.json file in the location you specify with output_s3_uri. For information about the schema for this file in the Schema for Constraints (constraints.json file).