The system performs well in offline tests.
Under real user traffic, errors appear.
Latency increases and predictions degrade.
The same model is running.
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The system performs well in offline tests.
Under real user traffic, errors appear.
Latency increases and predictions degrade.
The same model is running.
This happens because real-world usage introduces input patterns, concurrency, and timing effects not present in testing. Models trained on static datasets may fail when exposed to live data streams.
Serving systems also face numerical drift, caching issues, and resource contention, which affect prediction quality even if the model itself is unchanged.
Monitoring, data drift detection, and continuous retraining are necessary for stable real-world deployment. Common mistakes are No production monitoring, No retraining pipelineAssuming test data represents reality
The practical takeaway is that deployment is part of the learning system, not separate from it.