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

name

str

required

Human-readable name for the alert rule. Should be descriptive and unique within the model context.

model_id

UUID | str

required

UUID of the model this alert rule monitors. Must be a valid model that exists in the Fiddler platform.

metric_id

str | UUID

required

ID of the metric to monitor (e.g., “drift_score”, “accuracy”, “precision”, “recall”, custom metric IDs).

priority

Priority | str

required

Alert priority level (HIGH, MEDIUM, LOW). Determines urgency and routing of notifications.

compare_to

CompareTo | str

required

Comparison method for threshold evaluation:

BASELINE: Compare against a fixed baseline
TIME_PERIOD: Compare against previous time period
RAW_VALUE: Compare against absolute threshold

condition

AlertCondition | str

required

Alert condition (GT, LT, OUTSIDE_RANGE). Defines when the alert should trigger relative to the threshold.

bin_size

BinSize | str

required

Time aggregation window (HOUR, DAY, WEEK). Controls how data is grouped for metric calculation.

threshold_type

AlertThresholdAlgo | str

default:"AlertThresholdAlgo.MANUAL"

Threshold calculation method (MANUAL or AUTO). MANUAL uses user-defined thresholds, AUTO calculates dynamic thresholds based on historical data.

auto_threshold_params

dict[str, Any] | None

default:"None"

Parameters for automatic threshold calculation. Used when threshold_type is AUTO.

critical_threshold

float | None

default:"None"

Critical alert threshold value. Triggers high-priority notifications when exceeded.

columns

list[str] | None

default:"None"

List of feature columns to monitor. For feature-specific drift alerts. If None, monitors all features.

baseline_id

UUID | str | None

default:"None"

UUID of the baseline to compare against. Required when compare_to is BASELINE.

segment_id

UUID | str | None

default:"None"

UUID of the data segment to monitor. For segment-specific monitoring (optional).

compare_bin_delta

int | None

default:"None"

Number of time bins to compare against. Used with TIME_PERIOD comparison (e.g., 7 for week-over-week).

evaluation_delay

int

default:"0"

Delay in minutes before evaluating alerts. Helps avoid false positives from incomplete data.

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()

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

id_

UUID | str

required

The unique identifier (UUID) of the alert rule to retrieve. Can be provided as a UUID object or string representation.

Returns

The alert rule instance with all configuration and metadata populated from the server.

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()

Get a list of all alert rules in the organization.

Parameters

model_id

UUID | str

required

list from the specified model

metric_id

UUID | str | None

default:"None"

list rules set on the specified metric id

columns

list[str] | None

default:"None"

list rules set on the specified list of columns

ordering

list[str] | None

default:"None"

order result as per list of fields. [“-field_name”] for descending

Returns

paginated list of alert rules for the specified filters

delete()

Delete an alert rule.

create()

Create a new alert rule.

Returns

AlertRule

update()

Update an existing alert rule.

enable_notifications()

Enable notifications for an alert rule

disable_notifications()

Disable notifications for an alert rule

set_notification_config()

Set notification config for an alert rule

Parameters

emails

list[str] | None

default:"None"

list of emails

pagerduty_services

list[str] | None

default:"None"

list of pagerduty services

pagerduty_severity

str | None

default:"None"

severity of pagerduty

webhooks

list[UUID] | None

default:"None"

list of webhooks UUIDs

Returns

NotificationConfig object

get_notification_config()

Get notifications config for an alert rule

Returns

NotificationConfig object

​Example

​Parameters

​Example

​classmethod get()

​Parameters

​Returns

​Raises

​Example

​classmethod list()

​Parameters

​Returns

​delete()

​create()

​Returns

​update()

​enable_notifications()

​disable_notifications()

​set_notification_config()

​Parameters

​Returns

​get_notification_config()

​Returns

Example

Parameters

Example

classmethod get()

Parameters

Returns

Raises

Example

classmethod list()

Parameters

Returns

delete()

create()

Returns

update()

enable_notifications()

disable_notifications()

set_notification_config()

Parameters

Returns

get_notification_config()

Returns