The Docker container runs fine on my machine.
CI builds succeed without errors.
But once deployed, inference fails unexpectedly.
Logs aren’t very helpful either.
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The Docker container runs fine on my machine.
CI builds succeed without errors.
But once deployed, inference fails unexpectedly.
Logs aren’t very helpful either.
This usually points to environment mismatches rather than model issues.
Differences in CPU architecture, available system libraries, or runtime dependencies can cause failures that don’t appear locally. Even small version differences in NumPy or system packages can change behavior.
Check the base image used in production and ensure it matches local builds. Avoid “latest” tags and pin both system and Python dependencies explicitly.
Also confirm that model files are copied correctly and paths are consistent across environments.