With the increasing use of Industrial IoT solutions, one of the most essential decisions that should be made by companies concerns the choice between implementing AI models in the edge or cloud environment.
It is difficult to provide a universal recommendation as this decision greatly depends on such factors as the need for low latency, the reliability of the network, amount of data, the need for security, etc. Knowing the advantages of each architectural style will certainly help in building efficient and scalable systems.
Edge AI Definition
Edge AI means using machine learning models on devices or gateways situated near the industrial equipment.
Instead of uploading all the sensor measurements to the server situated remotely from the device, edge devices process the information and produce responses whenever needed.
Such solutions are widely used for:
- Machine anomaly detection
- Maintenance notifications
- Inspection based on vision algorithms
- Monitoring of equipment condition
- Safety monitoring of workers
What Is Cloud AI?
Cloud AI analyzes industrial data in centralized cloud systems.
Instead of analyzing individual machines, cloud technologies consider data from several production lines, plants, or geographical locations. Such approach allows implementing sophisticated algorithms that cannot be executed at the level of individual edge devices.
Examples of Cloud AI applications include:
- Trend analysis over time
- Asset management for the fleet
- Demand forecasting
- Production optimization
- Company-level reports
The advantage of such technology is efficient work with large datasets and continuous model improvement.
Why Hybrid Architectures Are the Future
More and more Industrial IoT systems utilize hybrid architectures that use edge and cloud computing simultaneously.
Here is an example:
- Data is collected by sensors.
- Anomalies are detected by edge devices.
- Critical alerts are created.
- Processed data is transferred to the cloud.
- Performance trends over time are identified in the cloud.
- AI models are updated and redeployed to edge devices.
Practical Design Considerations
In developing IIoT systems, engineers have several aspects to consider when choosing an AI solution.
Some of these questions are:
- How soon do you need to act?
- Can you work without access to the network?
- How much data is produced per minute?
- Do you have regulations or privacy policies concerning data storage?
- Would the deployment be extended to other sites in the future?
These are just some of the issues that will allow avoiding costly changes at a later time.
Concluding Thoughts
Edge AI and Cloud AI are often considered alternatives to each other, but in industry, they become supplementary tools.
Edge AI allows for making instant decisions about the actions related to the machinery on-site, and cloud AI supplies the necessary intelligence for process optimization in the company as a whole. This results in more efficient IIoT systems.
To those who may be interested in learning more about the technologies described here, the Aperture Venture Studio publishes technical articles on artificial intelligence, Industrial IoT, and intelligent industrial systems.
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