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MonitoringSchedule

Learn how to create, update, and manage AWS SageMaker MonitoringSchedules using Alchemy Cloud Control.

The MonitoringSchedule resource lets you manage AWS SageMaker MonitoringSchedules for monitoring model performance and data quality over time.

Create a basic monitoring schedule with required properties and one optional property.

import AWS from "alchemy/aws/control";
const basicMonitoringSchedule = await AWS.SageMaker.MonitoringSchedule("basicMonitoringSchedule", {
MonitoringScheduleName: "ModelPerformanceMonitoring",
MonitoringScheduleConfig: {
// Example configuration for monitoring
MonitoringJobDefinition: {
// Define the monitoring job here
BaselineConfig: {
BaselineParameters: {
// Parameters for baseline
},
Statistics: {
// Statistics for monitoring
}
},
// Additional configurations...
}
},
EndpointName: "my-endpoint" // Optional
});

Configure a monitoring schedule with detailed settings and tags for resource management.

const advancedMonitoringSchedule = await AWS.SageMaker.MonitoringSchedule("advancedMonitoringSchedule", {
MonitoringScheduleName: "AdvancedModelMonitoring",
MonitoringScheduleConfig: {
MonitoringJobDefinition: {
BaselineConfig: {
BaselineParameters: {
// Define baseline parameters
},
Statistics: {
// Define statistics
}
},
// Additional configurations...
}
},
Tags: [
{
Key: "Environment",
Value: "Production"
},
{
Key: "Project",
Value: "ModelMonitoring"
}
]
});

Create a monitoring schedule with options to handle failure scenarios.

const errorHandlingMonitoringSchedule = await AWS.SageMaker.MonitoringSchedule("errorHandlingMonitoringSchedule", {
MonitoringScheduleName: "ErrorHandlingMonitoring",
MonitoringScheduleConfig: {
MonitoringJobDefinition: {
BaselineConfig: {
BaselineParameters: {
// Baseline parameters
},
Statistics: {
// Statistics
}
},
// Additional configurations...
}
},
FailureReason: "Invalid configuration provided" // Optional, to log the failure reason
});