Different teams trained models independently.
Each performs well in certain cases.
Now deployment is messy.
Choosing one feels arbitrary.
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Different teams trained models independently.
Each performs well in certain cases.
Now deployment is messy.
Choosing one feels arbitrary.
This is a governance and orchestration problem.
Use clear evaluation criteria aligned with business goals. In some cases, ensemble or routing strategies perform better than a single model.
Centralize deployment ownership and define decision rules for model selection.
Avoid letting models compete silently in production.
Common mistakes include:Deploying models without ownership, Lacking comparison benchmarks andAllowing configuration sprawl
The takeaway is that model choice should be intentional, not political.