1. Real-time Monitoring Dashboard for ML Model Performance


    To create a real-time monitoring dashboard for ML (Machine Learning) model performance, you typically need to consider both the numerical data generated by the model (such as predictions, accuracy, and loss metrics) and the resource usage of the infrastructure running the model (like compute and memory utilization).

    AWS offers several services that could help build a real-time monitoring dashboard. For ML model performance, Amazon SageMaker provides capabilities for model training, deployment, and monitoring, and Amazon CloudWatch can be used for monitoring the operational metrics of your ML models and the infrastructure.

    Here is a Pulumi program written in Python that sets up a real-time monitoring dashboard for ML model performance using AWS services. We will use aws_native.sagemaker.MonitoringSchedule for model monitoring and aws.cloudwatch.Dashboard for the dashboard front end.

    import json import pulumi import pulumi_aws_native as aws_native import pulumi_aws as aws # Define the SageMaker monitoring schedule with a specific endpoint for your model. # Replace 'your-model-endpoint' with the actual SageMaker model endpoint name. monitoring_schedule = aws_native.sagemaker.MonitoringSchedule("MonitoringSchedule", monitoring_schedule_name="MyModelPerformanceSchedule", monitoring_schedule_config=aws_native.sagemaker.MonitoringScheduleConfigArgs( monitoring_job_definition=aws_native.sagemaker.MonitoringJobDefinitionArgs( baseline_config=aws_native.sagemaker.BaselineConfigArgs( constraints_resource=aws_native.sagemaker.ConstraintsResourceArgs( s3_uri="s3://your-bucket/constraints.json" ), statistics_resource=aws_native.sagemaker.StatisticsResourceArgs( s3_uri="s3://your-bucket/statistics.json" ), ), monitoring_inputs=[ aws_native.sagemaker.MonitoringInputArgs( endpoint_input=aws_native.sagemaker.EndpointInputArgs( endpoint_name="your-model-endpoint", local_path="/opt/ml/processing/input", ) ) ], monitoring_resources=aws_native.sagemaker.MonitoringResourcesArgs( cluster_config=aws_native.sagemaker.ClusterConfigArgs( instance_count=1, instance_type="ml.m5.large", volume_size_in_gb=30, ) ), monitoring_output_config=aws_native.sagemaker.MonitoringOutputConfigArgs( monitoring_outputs=[ aws_native.sagemaker.MonitoringOutputArgs( s3_output=aws_native.sagemaker.S3OutputArgs( s3_uri="s3://your-bucket/monitoring-output", local_path="/opt/ml/processing/output", s3_upload_mode="EndOfJob", ) ) ] ), role_arn=pulumi.Config('aws').require('roleArn') # The ARN of the role with permissions for SageMaker to access S3 resources. ), monitoring_type="DataQuality" ), tags=[ aws_native.sagemaker.TagArgs( key="Purpose", value="MLModelMonitoring" ) ] ) # Define Amazon CloudWatch dashboard for monitoring ML model performance. dashboard_body = { "widgets": [ { "type": "metric", "x": 0, "y": 0, "width": 24, "height": 6, "properties": { "metrics": [ ["AWS/SageMaker", "ModelLatency", "EndpointName", "your-model-endpoint"], [".", "Invocations", ".", "."], [".", "Invocation4XXErrors", ".", "."], [".", "Invocation5XXErrors", ".", "."], [".", "ModelError", ".", "."] ], "view": "timeSeries", "stacked": False, "region": "us-west-2", # Replace this with the region you are deploying resources in. "stat": "Average", "period": 300 } } # Add other widgets as needed for additional metrics or logs. ] } cloudwatch_dashboard = aws.cloudwatch.Dashboard("CloudWatchDashboard", dashboard_name="MyMLModelPerformanceDashboard", dashboard_body=json.dumps(dashboard_body) ) # Exports pulumi.export('SageMakerMonitoringScheduleName', monitoring_schedule.monitoring_schedule_name) pulumi.export('CloudWatchDashboardName', cloudwatch_dashboard.dashboard_name)

    This program sets two key AWS resources:

    1. aws_native.sagemaker.MonitoringSchedule: This resource creates a monitoring schedule for a SageMaker model endpoint. It periodically captures inference data and compares it to a set of baseline statistics and constraints to identify deviations in model quality. Replace 'your-model-endpoint', 's3://your-bucket/constraints.json', 's3://your-bucket/statistics.json', and 's3://your-bucket/monitoring-output' with your specific details.

    2. aws.cloudwatch.Dashboard: Here we create a CloudWatch Dashboard programmatically with a simple layout that contains a widget visualizing certain metrics from SageMaker such as ModelLatency, Invocations, and error counts. The metric widget definition should be customized based on the specific metrics that you want to monitor. The dashboard_body is defined as a Python dictionary which is then serialized to JSON, which is the format required by CloudWatch for dashboard creation.

    Feel free to add or modify widgets to include other relevant metrics as necessary. Update the region in the CloudWatch widget properties to match where your resources are deployed ("region": "us-west-2" in the example).

    To use this program:

    1. Replace placeholders with actual values corresponding to your AWS setup (e.g., your-model-endpoint, S3 URIs).
    2. Make sure that an IAM role with the necessary permissions for SageMaker to access S3 resources is configured. The ARN of this role should be provided as roleArn in your Pulumi configuration.

    Remember to have the Pulumi AWS provider configured with credentials for an account that has permissions to create these resources. After running this Pulumi program with pulumi up, it will set up the monitoring schedule and the dashboard, providing you with the names as stack outputs.