7.2. Alerting

What is AI/ML Alerting?

AI/ML Alerting is a crucial aspect of monitoring that involves sending notifications to relevant stakeholders when specific conditions are met, signifying potential issues or deviations in the behavior or performance of a deployed machine learning model. These alerts act as early warning systems, enabling timely intervention and mitigation of problems before they significantly impact business outcomes or user experience.

Effective alerting relies on:

Defining Alert Conditions: Establishing clear criteria that trigger alerts, such as significant drops in model accuracy, exceeding error thresholds, or the detection of data drift.
Identifying Recipients: Specifying individuals or teams responsible for responding to alerts, ensuring that notifications reach the right people.
Selecting Communication Channels: Choosing appropriate methods for delivering alerts, whether it's email, Slack messages, system notifications, or specialized alerting platforms.

Why do you need Alerting for AI/ML?

Alerting plays a critical role in maintaining the reliability and value of AI/ML solutions in production environments. Its main benefits include:

Rapid Response to Issues: Alerts provide a mechanism for quickly informing stakeholders of problems, reducing the time it takes to diagnose and mitigate them.
Proactive Issue Mitigation: By detecting issues early, alerting systems enable proactive interventions, preventing minor problems from escalating into major disruptions or failures.
Improved Decision Making: Alerts can provide valuable data and insights that help teams make informed decisions on model retraining, hyperparameter adjustments, or other optimizations.
Increased System Uptime: By minimizing downtime and ensuring model accuracy, alerting contributes to the overall stability and reliability of AI/ML applications.

Effective AI/ML alerting acts as a safety net, safeguarding against model decay and unforeseen circumstances that could impact the performance or behavior of deployed models.

Which conditions should trigger an alert?

Deciding which conditions should trigger an alert involves considering the specific context of the model and its impact on business goals. Here are some common scenarios:

Performance Degradation: When a model's accuracy, precision, recall, or other key metrics drop significantly below predefined thresholds.
Data Drift: When the distribution or characteristics of the input data change significantly compared to the data used for training the model, which might signal a need for retraining.
Bias Detection: When the model demonstrates bias towards certain groups or categories, which could lead to unfair or unethical outcomes.
System Errors: When errors or exceptions occur within the model serving infrastructure or dependencies, potentially impacting service availability.
Resource Utilization: When the model's resource consumption, such as CPU, memory, or network bandwidth, exceeds predefined limits, which might signal performance bottlenecks.

Which platforms can send alerts?

To implement an effective alerting system, you need to choose communication channels that align with your team’s workflow and preferences. Here are some common options for sending AI/ML alerts:

Slack and Discord: Suitable for real-time team communication, these messaging platforms allow for instant notifications, discussions, and collaboration among team members.
Datadog: A popular monitoring and observability platform, it provides comprehensive alerting capabilities for various system and application metrics, including those related to AI/ML models.
Statuspal: This platform specializes in status page monitoring and incident communication, making it useful for notifying users about any disruptions or downtime related to AI/ML services.
PagerDuty: A popular incident management platform that can be used for routing AI/ML alerts to the right team members, escalating issues if necessary, and ensuring that incidents are addressed promptly.

How can you implement Alerting (local demo)?

The MLOps Python Package includes a simple alerting service based on the plyer library, which provides cross-platform notifications. This service can be used to send alerts to the user's desktop, offering a basic but effective way to notify developers about events during model training, tuning, or deployment.

Here's how you can use the alerting service:

Configure the AlertsService: Set the enable parameter to True to activate the alerting feature. You can also customize the app_name and timeout parameters as needed.
```
from bikes.io import services

alerts_service = services.AlertsService(enable=True, app_name="Bikes", timeout=5)
```
Integrate with a Job: Include the alerts_service as a parameter within a job definition. For instance, you can add it to the TrainingJob to receive a notification when model training is complete.
```
from bikes import jobs

training_job = jobs.TrainingJob(
    ...,
    alerts_service=alerts_service,
)
```

Send Alerts: Use the alerts_service.notify() method within the job's run() method to trigger notifications based on specific conditions.

# Within the TrainingJob's run() method
# ... (training logic)
self.alerts_service.notify(title="Training Complete", message=f"Model version: {model_version.version}")

Disable for Production: Remember to disable desktop notifications (enable=False) in production settings to avoid disrupting users. Instead, integrate with more appropriate alerting platforms as mentioned earlier.

What are the Best Practices for AI/ML Alerting?

Prioritize Alerts: Categorize alerts by severity to ensure that critical issues receive immediate attention, while less urgent notifications can be handled later.
Provide Context: Include relevant information in alert messages, such as the affected model, specific metrics, and potential causes.
Avoid Alert Fatigue: Strike a balance between informing stakeholders and overwhelming them with too many notifications.
Automate Actions: Where possible, automate responses to alerts, such as triggering model retraining, scaling infrastructure, or rolling back to a previous model version.
Regularly Review and Update: Periodically assess the effectiveness of your alerting system and adjust alert conditions, recipients, and channels as needed based on feedback and experience.