AlertRule - Fiddler Documentation

Alert rule for automated monitoring and alerting in ML systems. An AlertRule defines conditions that automatically trigger notifications when ML model metrics exceed specified thresholds. Alert rules are essential for proactive monitoring of model performance, data drift, and operational issues.

Example

# Create feature drift alert
drift_alert = AlertRule(
    name="credit_score_drift",
    model_id=model.id,
    metric_id="drift_score",
    priority=Priority.HIGH,
    compare_to=CompareTo.BASELINE,
    condition=AlertCondition.GT,
    bin_size=BinSize.HOUR,
    critical_threshold=0.8,
    warning_threshold=0.6,
    baseline_id=baseline.id,
    columns=["credit_score", "income"]
).create()

# Create performance degradation alert
perf_alert = AlertRule(
    name="accuracy_drop",
    model_id=model.id,
    metric_id="accuracy",
    priority=Priority.MEDIUM,
    compare_to=CompareTo.TIME_PERIOD,
    condition=AlertCondition.LESSER,
    bin_size=BinSize.DAY,
    critical_threshold=0.85,
    compare_bin_delta=7  # Compare to 7 days ago
).create()

# Configure notifications
drift_alert.set_notification_config(
    emails=["[ml-team@company.com](mailto:ml-team@company.com)", "[data-team@company.com](mailto:data-team@company.com)"],
    pagerduty_services=["ML_ALERTS"],
    pagerduty_severity="critical"
)

Alert rules continuously monitor metrics and trigger notifications when thresholds are exceeded. Use appropriate evaluation delays to avoid false positives from temporary data fluctuations.

Initialize an AlertRule instance. Creates an alert rule configuration for automated monitoring of ML model metrics. The alert rule defines conditions that trigger notifications when thresholds are exceeded, enabling proactive monitoring of model performance and data quality.

Parameters

Parameter	Type	Required	Default	Description
`name`	`str`	✗	`None`	Human-readable name for the alert rule. Should be descriptive and unique within the model context.
`model_id`	`UUID	str`	✗	`None`
`metric_id`	`str	UUID`	✗	`None`
`priority`	`Priority	str`	✗	`None`
`compare_to`	`CompareTo	str`	✗	`None`
`condition`	`AlertCondition	str`	✗	`None`
`bin_size`	`BinSize	str`	✗	`None`
`threshold_type`	`AlertThresholdAlgo	str`	✗	`None`
`auto_threshold_params`	`dict[str, Any] \| None`	✗	`None`	Parameters for automatic threshold calculation. Used when threshold_type is `AUTO`.
`critical_threshold`	`float	None`	✗	`None`
`warning_threshold`	`float	None`	✗	`None`
`columns`	`list[str] \| None`	✗	`None`	List of feature columns to monitor. For feature-specific drift alerts. If None, monitors all features.
`baseline_id`	`UUID	str	None`	✗
`segment_id`	`UUID	str	None`	✗
`compare_bin_delta`	`int	None`	✗	`None`
`evaluation_delay`	`int`	✗	`None`	Delay in minutes before evaluating alerts. Helps avoid false positives from incomplete data.
`category`	`str	None`	✗	`None`

Example

# Feature drift alert with baseline comparison
drift_alert = AlertRule(
    name="income_drift_detection",
    model_id=model.id,
    metric_id="drift_score",
    priority=Priority.HIGH,
    compare_to=CompareTo.BASELINE,
    condition=AlertCondition.GT,
    bin_size=BinSize.HOUR,
    critical_threshold=0.8,
    warning_threshold=0.6,
    baseline_id=baseline.id,
    columns=["income", "credit_score"],
    evaluation_delay=15,  # 15 minute delay
    category="data_quality"
)

# Performance monitoring with time comparison
perf_alert = AlertRule(
    name="weekly_accuracy_check",
    model_id=model.id,
    metric_id="accuracy",
    priority=Priority.MEDIUM,
    compare_to=CompareTo.TIME_PERIOD,
    condition=AlertCondition.LESSER,
    bin_size=BinSize.DAY,
    critical_threshold=0.85,
    compare_bin_delta=7,  # Compare to 7 days ago
    category="performance"
)

After initialization, call create() to persist the alert rule to the Fiddler platform. Alert rules begin monitoring immediately after creation.

classmethod get(id_)

Retrieve an alert rule by its unique identifier. Fetches an alert rule from the Fiddler platform using its UUID. This method returns the complete alert rule configuration including thresholds, notification settings, and monitoring status.

Parameters

Parameter	Type	Required	Default	Description
`id_`	`UUID	str`	✗	`None`

Returns

The alert rule instance with all configuration and metadata populated from the server. Return type: AlertRule

Raises

NotFound — If no alert rule exists with the specified ID.
ApiError — If there’s an error communicating with the Fiddler API.

Example

# Retrieve alert rule by ID
alert_rule = AlertRule.get(id_="550e8400-e29b-41d4-a716-446655440000")
print(f"Alert: {alert_rule.name}")
print(f"Metric: {alert_rule.metric_id}")
print(f"Priority: {alert_rule.priority}")
print(f"Critical threshold: {alert_rule.critical_threshold}")

# Check notification configuration
notification_config = alert_rule.get_notification_config()
print(f"Email recipients: {notification_config.emails}")

This method makes an API call to fetch the latest alert rule configuration from the server, including any recent threshold or notification updates.

classmethod list(model_id, metric_id=None, columns=None, baseline_id=None, ordering=None)

Get a list of all alert rules in the organization.

Parameters

Parameter	Type	Required	Default	Description
`model_id`	`UUID	str`	✗	`None`
`metric_id`	`UUID	str	None`	✗
`columns`	`list[str] \| None`	✗	`None`	list rules set on the specified list of columns
`baseline_id`	`UUID	str	None`	✗
`ordering`	`list[str] \| None`	✗	`None`	order result as per list of fields. [“-field_name”] for descending

Returns

paginated list of alert rules for the specified filters Return type: Iterator[AlertRule]

delete()

Delete an alert rule. Return type: None

create()

Create a new alert rule. Return type: AlertRule

update()

Update an existing alert rule. Return type: None

enable_notifications()

Enable notifications for an alert rule Return type: None

disable_notifications()

Disable notifications for an alert rule Return type: None

set_notification_config()

Set notification config for an alert rule

Parameters

Parameter	Type	Required	Default	Description
`emails`	`list[str] \| None`	✗	`None`	list of emails
`pagerduty_services`	`list[str] \| None`	✗	`None`	list of pagerduty services
`pagerduty_severity`	`str	None`	✗	`None`
`webhooks`	`list[UUID] \| None`	✗	`None`	list of webhooks UUIDs

Returns

NotificationConfig object Return type: NotificationConfig

get_notification_config()

Get notifications config for an alert rule

Returns

NotificationConfig object Return type: NotificationConfig

Documentation Index

​Example

​Parameters

​Example

​classmethod get(id_)

​Parameters

​Returns

​Raises

​Example

​classmethod list(model_id, metric_id=None, columns=None, baseline_id=None, ordering=None)

​Parameters

​Returns

​delete()

​create()

​update()

​enable_notifications()

​disable_notifications()

​set_notification_config()

​Parameters

​Returns

​get_notification_config()

​Returns

Example

Parameters

Example

classmethod get(id_)

Parameters

Returns

Raises

Example

classmethod list(model_id, metric_id=None, columns=None, baseline_id=None, ordering=None)

Parameters

Returns

delete()

create()

update()

enable_notifications()

disable_notifications()

set_notification_config()

Parameters

Returns

get_notification_config()

Returns