Enabling Predictor Monitoring

PDFRSS

You can enable predictor monitoring when you create the predictor, or you can enable it for an existing predictor.

Note

Predictor monitoring is only available for AutoPredictors. You can upgrade existing legacy predictors to AutoPredictor. See Upgrading to AutoPredictor.

Enabling Predictor Monitoring for a New Predictor

You can enable predictor monitoring for a new predictor with the console, AWS CLI, AWS SDKs, and the CreateAutoPredictor operation.

Console

To enable Predictor monitoring

1. Sign in to the AWS Management Console and open the Amazon Forecast console at https://console.aws.amazon.com/forecast/.

2. From Dataset groups, choose your dataset group.

3. In the navigation pane, choose Predictors.

4. Choose Train new predictor.

5. In the Predictor configuration section, choose Enable monitoring.

6. Provide values for the following mandatory fields:

   • Name - a unique predictor name.

   • Forecast frequency - the granularity of your forecasts.

   • Forecast horizon - The number of time steps to forecast.

7. Choose Start to create an auto predictor with monitoring enabled. You’ll see monitoring results as you use the predictor to generate forecasts and then import more data.

Python

To enable predictor monitoring for a new predictor with the SDK for Python (Boto3), use the create_auto_predictor method and provide a monitor name in the MonitoringConfig.

The following code creates an auto predictor that makes predictions for 24 (ForecastHorizon) days (ForecastFrequency) in the future, and specifies MyPredictorMonitor as the MonitorName. After you generate a forecast and then import more data, you can view the results of predictor monitoring. For more information about retrieving results, see Viewing Monitoring Results.

For information on required and optional parameters for creating a predictor see CreateAutoPredictor.

import boto3
                            
forecast = boto3.client('forecast')

create_predictor_response = forecast.create_auto_predictor(
    PredictorName = 'predictor_name',
    ForecastHorizon = 24,
    ForecastFrequency = 'D',
    DataConfig = {
        "DatasetGroupArn": "arn:aws:forecast:region:account:dataset-group/datasetGroupName"
    },
    MonitorConifg = {
        "MonitorName": "MyMonitorName"
    }
)

Enabling Predictor Monitoring for an Existing Predictor

You can enable predictor monitoring for an existing predictor with the console, AWS CLI, and AWS SDKs.

Console

To enable predictor monitoring

1. Sign in to the AWS Management Console and open the Amazon Forecast console at https://console.aws.amazon.com/forecast/.

2. From Dataset groups, choose your dataset group.

3. In the navigation pane, choose Predictors.

4. Choose your predictor.

5. Navigate to the Monitoring tab.

6. In the Monitoring details section, choose Start monitoring

   When the Monitoring status is Active, predictor monitoring is enabled. After you generate a forecast and then import more data, you can view the results of predictor monitoring. For more information see Viewing Monitoring Results

Python

To enable predictor monitoring for an existing predictor with the SDK for Python (Boto3), use the create_monitor method. Specify a name for the monitoring, and for ResourceArn specify the Amazon Resource Name (ARN) for the predictor to monitor. Use the describe_monitor method and provide the monitor ARN to get the status of the monitor. After you generate a forecast and then import more data, you can view the results of predictor monitoring. For more information see Viewing Monitoring Results.

For information on required and optional parameters, see the CreateMonitor and DescribeMonitor.

import boto3
                            
forecast = boto3.client('forecast')

create_monitor_response = forecast.create_monitor(
    MonitorName = 'monitor_name',
    ResourceArn = 'arn:aws:forecast:region:accountNumber:predictor/predictorName'
)

monitor_arn = create_monitor_response['MonitorArn']

describe_monitor_response = forecast.describe_monitor(
    MonitorArn = monitor_arn
)
print("Monitor status: " + describe_monitor_response['Status'])