My model gives great accuracy on my laptop.
When deployed on a server, predictions become inconsistent.
The same input sometimes produces different outputs.
Nothing crashes, but the behavior is unreliable.
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My model gives great accuracy on my laptop.
When deployed on a server, predictions become inconsistent.
The same input sometimes produces different outputs.
Nothing crashes, but the behavior is unreliable.
This happens when training and production environments are not identical.
Differences in preprocessing, floating-point precision, library versions, or hardware can change numerical behavior in neural networks.
Make sure the same versions of Python, CUDA, PyTorch, and preprocessing code are used. Always export the full inference pipeline, not just the model weights.
Common mistakes:
Rebuilding tokenizers in production
Different image resize algorithms
Mixing CPU and GPU behavior
The practical takeaway is that models do not generalize across environments unless the full pipeline is preserved.