Features
System health monitoring
The starter kit provides a reference implementation for system health monitoring use cases. The solution is designed to reliably evaluate system health and detect anomalies in a large number of IoT metrics received from multiple sensors and machines.
Multiple system types
The starter kit includes a design guide that helps determine the right model type based on the properties of the monitored system and availability of the labeled data.
Power of Vertex AI AutoML
The starter kit leverages native Vertex AI AutoML components to evaluate system health. AutoML sharply reduces the feature and model engineering efforts, enabling developers to focus on more business-oriented problems.
Open source
We use only native Google Cloud services and open source libraries, so the starter kit can be easily extended and customized.
Use cases
The starter kit provides out of the box system health monitoring and anomaly detection pipelines. These reference pipelines can be used as a starting point for implementing a broader range of use cases related to Industrial IoT and Smart Manufacturing.
Defect detection
Anomaly classification
Remaining useful life estimation
Predictive maintenance
Industries
System health monitoring and anomaly detection capabilities are important in many industries. The starter kit includes several templates that cover some of the most relevant ones.
Discrete manufacturing
Monitor equipment health and prevent downtimes.
Continuous manufacturing
Continuosly control the product quality and detect anomalies.
Energy
Get the most value from your smart grid investments.
Oil and gas
Implement sensor-based tank monitoring and asset monitoring.
Transportation
Monitor electric vehicles and optimize EV charging.
Life sciences
Monitor production processes, ensure quality and compliance.
Why develop an IoT analytics solution in Google Cloud?
How it works
The starter kit includes health scoring models and detection algorithms that make binary decisions and generate alerts.
Learn more
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