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Monitors

Monitors let you easily set custom alerts to track your model's health and performance over time, ensuring your models are on track. Once configured, AryaXAI can notify users when it detects identified drift in the data, enabling proactive intervention to maintain model accuracy and reliability. Users can also choose a custom compute required for the alert.

You can set monitors to detect data and target drift or model performance degradation. To install the package, you can use the following functions:

To list all monitoring Triggers created:


# list monitoring triggers
project.monitoring_triggers()

You can also use the help function to create monitoring triggers for Data Drift, Target Drift, and Model Performance using payload


help(project.create_monitoring_trigger)

Alerts

Monitors that have been previously set up will appear as alerts once triggered. AryaXAI alerts enable users to get detailed root cause analysis of triggered alerts and pinpoint factors contributing to model degradation. Users can set up alerts to detect data and target drift, performance degradation, anomalies, etc. 

Delete the monitoring trigger:


# delete monitoring trigger
project.delete_monitoring_trigger('test trigger 5')

Fetch details of executed triggers: 


# Fetch details of executed Triggers
project.alerts()

View email notifications

When setting up monitors, users can specify the email addresses where they want to receive notifications for triggered alerts. For example:


 {
                "trigger_type": ""  #["Data Drift", "Target Drift", "Model Performance"]
                "trigger_name": "",
                "mail_list": [],
                "frequency": "",   #['daily','weekly','monthly','quarterly','yearly']
                "stat_test_name": "",
                "stat_test_threshold": 0,
                "datadrift_features_per": 7,
                "features_to_use": [],
                "date_feature": "",
                "baseline_date": { "start_date": "", "end_date": ""},
                "current_date": { "start_date": "", "end_date": ""},
                "base_line_tag": "",
                "current_tag": ""
            } 

Here, 'mail_list' is where you specify the email addresses of the users who want to be notified.

Monitors

Monitors let you easily set custom alerts to track your model's health and performance over time, ensuring your models are on track. Once configured, AryaXAI can notify users when it detects identified drift in the data, enabling proactive intervention to maintain model accuracy and reliability. Users can also choose a custom compute required for the alert.

You can set monitors to detect data and target drift or model performance degradation. To install the package, you can use the following functions:

To list all monitoring Triggers created:


# list monitoring triggers
project.monitoring_triggers()

You can also use the help function to create monitoring triggers for Data Drift, Target Drift, and Model Performance using payload


help(project.create_monitoring_trigger)

Alerts

Monitors that have been previously set up will appear as alerts once triggered. AryaXAI alerts enable users to get detailed root cause analysis of triggered alerts and pinpoint factors contributing to model degradation. Users can set up alerts to detect data and target drift, performance degradation, anomalies, etc. 

Delete the monitoring trigger:


# delete monitoring trigger
project.delete_monitoring_trigger('test trigger 5')

Fetch details of executed triggers: 


# Fetch details of executed Triggers
project.alerts()

View email notifications

When setting up monitors, users can specify the email addresses where they want to receive notifications for triggered alerts. For example:


 {
                "trigger_type": ""  #["Data Drift", "Target Drift", "Model Performance"]
                "trigger_name": "",
                "mail_list": [],
                "frequency": "",   #['daily','weekly','monthly','quarterly','yearly']
                "stat_test_name": "",
                "stat_test_threshold": 0,
                "datadrift_features_per": 7,
                "features_to_use": [],
                "date_feature": "",
                "baseline_date": { "start_date": "", "end_date": ""},
                "current_date": { "start_date": "", "end_date": ""},
                "base_line_tag": "",
                "current_tag": ""
            } 

Here, 'mail_list' is where you specify the email addresses of the users who want to be notified.