As IIoT applications gain more and more traction, there is a growing necessity for developers to decide whether they should place their AI algorithms at the edge or in the cloud.
Of course, it is impossible to make a blanket statement because it depends on the specifics of the situation.
Nevertheless, understanding the benefits and drawbacks of edge vs. cloud architectures will help developers create highly efficient solutions.
What Is Edge AI?
By mentioning edge AI, we mean running machine learning algorithms on hardware close to the location where the data is produced. The hardware can range from an industrial gateway, embedded system, smart camera, programmable logic controller (PLC), etc.
All data gathered by sensors is not transferred to the cloud server. Rather, the predictions are made directly at the hardware end and acted upon immediately.
Common use cases for edge AI are:
- Equipment anomaly detection
- Computer vision-based quality inspection
- Predictive maintenance notifications
- Worker safety surveillance
- Environment monitoring
Response time for edge AI is usually measured in milliseconds.
What Is Cloud AI?
Cloud AI analyzes data on cloud-based infrastructure.
Data is collected by devices and sent to cloud services for analysis using machine learning algorithms on larger datasets and with more computing power.
Cloud AI is ideal for:
- Training of machine learning models
- Analysis of historical trends
- Reporting across multiple sites
- Visualization via business intelligence dashboards
- Predictive analytics in the long run
Additionally, cloud infrastructure makes it easier to update models and manage them centrally from thousands of connected devices.
When Should Developers Consider Edge AI?
Edge AI comes into play when quick decisions have to be made.
Let us take an example of the use of computer vision in detecting problems in manufacturing process. A delay of a few seconds in the cloud might result in letting faulty products to proceed in production.
Inference done locally leads to immediate detection and correction.
Also, edge AI is preferred in cases where there is:
- Uncertain internet connection
- Restrictions in sending data due to privacy concerns
- Expensive bandwidth
- No continuous cloud communication possible
What Are the Instances Where Using Cloud AI Would Be More Appropriate?
Cloud-based systems will always be relevant for those tasks that require considerable computation capabilities.
Such instances are:
- Model training in machine learning
- Data aggregation from different locations
- Detection of long-term trends
- Optimization of fleet operation
- Corporate reporting
Using cloud systems will also facilitate collaboration between various specialists such as data scientists, developers, and operators who can work using one database.
The Hybrid Approach
There are many companies operating in the industrial sector which use a hybrid strategy.
With this approach:
- Local devices conduct real-time inference.
- Important events are sent to the cloud.
- Historical data is centralized.
- Cloud systems train models.
- Models are deployed back to local devices.
Challenges to Consider When Choosing an Architecture
Choosing an architecture is not only about performance.
Among other considerations are:
- Management of device lifecycles
- Deployment and versioning of models
- Cybersecurity
- Data governance
- Observability and monitoring
- Limitations imposed by hardware
- Costs
Considering these aspects in advance will help avoid problems associated with future maintenance of deployments.
Thinking Beyond Technology
All architecture choices should be driven by business needs rather than technological trends.
If fast reaction is key, it may be a good idea to choose edge computing. Cloud-based infrastructure might be preferable for enterprise-scale analytics.
Successful Industrial IoT solutions often use a combination of both and delegate different parts to architectures they are best suited for.
For readers interested in how AI, IoT, and industrial innovations influence modern technological solutions, Aperture Venture Studio offers additional perspectives on creating AI-powered industrial businesses.
Conclusion
Edge vs Cloud AI is not a matter of who wins—the point is using the proper technology on a specific task.
The more advanced Industrial IoT solutions become, the better equipped the developers, who know both architecture, are to create highly scalable and effective applications.
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