Sensor Data → Anomaly Detection → Automated Alerts
Monitor IoT sensor networks in real-time, detect anomalies or changes using AI, and automatically trigger alerts and response actions for infrastructure monitoring.
Workflow Steps
AWS IoT Core
Collect and stream sensor data
Set up IoT device connections to stream real-time data from environmental sensors, equipment monitors, or infrastructure sensors into AWS IoT Core.
AWS SageMaker
Deploy anomaly detection models
Train and deploy machine learning models in SageMaker to detect unusual patterns, threshold breaches, or predictive maintenance signals in the incoming sensor data.
AWS Lambda
Process alerts and determine severity
Create serverless functions that evaluate detected anomalies, classify severity levels, and prepare structured alert data with context and recommended actions.
Zapier
Route alerts to appropriate channels
Configure Zapier to automatically send alerts via Slack, email, SMS, or ticketing systems based on severity level and escalation rules.
PagerDuty
Manage incident response workflow
Create incidents in PagerDuty with full context, assign to on-call engineers, track response times, and maintain audit trails for critical infrastructure alerts.
Workflow Flow
Step 1
AWS IoT Core
Collect and stream sensor data
Step 2
AWS SageMaker
Deploy anomaly detection models
Step 3
AWS Lambda
Process alerts and determine severity
Step 4
Zapier
Route alerts to appropriate channels
Step 5
PagerDuty
Manage incident response workflow
Why This Works
Creates a complete monitoring ecosystem that scales from data collection to incident response, reducing manual monitoring effort and improving response times.
Best For
Infrastructure monitoring, environmental sensing, predictive maintenance, smart city applications
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