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Could performance or reliability issues emerge when scaling the AI system across environments?
Could performance or reliability issues emerge when scaling the AI system across environments?
Can your algorithm scale in performance from the data it learned on to real data? In online situations the rate at which data comes into the model may not align with the rate of anticipated data arrival. This can lead to both outright ML system failure and to a system that becomes unstable or exhibits feedback loops. Source: BerryVilleiML
If you answered Yes then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Determine the expected rate of data arrival and test the model under similar conditions.
- Implement measures to make your model scalable.