Nothing changed in the code logic.
Only the ML framework version was upgraded.
Yet predictions shifted slightly.
This caused unexpected regressions?
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Framework upgrades can change numerical behavior.
Optimizations, default settings, and backend implementations may differ between versions. These changes can affect floating-point precision or execution order.Always validate models after upgrades using fixed test datasets. If differences matter, pin versions or retrain models explicitly.
Common mistakes include: Assuming backward compatibility, Skipping post-upgrade validation and upgrading multiple components at once
The takeaway is that ML dependencies are part of model behavior.