Factories can run AI models close to machines, in a central data center or in the cloud. This decision affects latency, cost, network dependency, maintenance and data governance.
Edge AI advantages
Processing near the camera or machine may reduce response delay and keep certain raw data on site. This can be useful for time-sensitive visual inspection or local condition monitoring.
Cloud AI advantages
Cloud resources can make model training, centralized analysis and multi-site deployment easier. However, operations that need very low latency or must continue offline may need a local fallback.
Hybrid design
A factory might inspect images locally and send only approved summaries for central reporting. Decide which data must leave the site, who can access it and whether the connection is resilient enough.
Select using constraints
Measure acceptable latency, expected data volume, model updates, hardware replacement and recovery requirements. Avoid assuming that edge is inherently secure or cloud is inherently unreliable.
Conclusion
The right AI architecture starts with process constraints and security needs, not technology fashion.
To see how connected factory software can support your manufacturing workflows, request a Factovare demonstration.