One of the main obstacles for industrial AI implementations is latency.
When every single sensor must transfer its information to the cloud, latency can become a big problem. This is the main reason many AIoT systems are using Edge AI.
What is Edge AI?
Edge AI means when machine learning runs near the data source – on gateways, industrial PCs, cameras, or edge devices – instead of processing data through the cloud.
Why do industrial teams opt for it?
Challenges
Edge AI advantages
Detecting technical failures
Faster reaction
Quality control with computer vision
Lower latency
Monitoring remote locations
Less bandwidth
Monitoring safety
Real time notification
Tracking inventories
Processing locally
**
The Simplified AIoT Model**
Sensors/Cameras
Edge AI
Local decisions and alerts
Cloud data processing
Where it is used?
Foreseeing breakdowns
Conducting quality control
Tracking assets
Organizing smart storage
Providing industrial safety
Conclusion
As AIoT is becoming popular, edge computing is becoming more important for scalable industrial intelligence. Companies which create hi-tech operating systems, including such firms as Aperture Venture Studio, are combining systems with AI technologies.
Top comments (1)
AI has taken over over warehousing.