MLOps
What is shadow mode deployment?
Shadow mode is running a new model live against real production data while nobody acts on its output - so you can measure it in the real world before it can do damage.
What it catches that offline evaluation does not
Offline evaluation tests the model on historical data through the training pipeline. Shadow mode tests the whole production path: the adapter, the live feature computation, the latency, the missing-value handling, the edge cases your historical extract quietly dropped. A large fraction of ML failures live in that gap, not in the model.
How to run it
Deploy the scoring service, feed it live data, write scores to a log, and show nothing to the team acting on them. Run for a full business cycle - typically two weeks for retention work. Compare the score distribution against offline expectations, and compare the model's ranking against what the team actually did on its own.
The comparison that matters
The interesting question is not whether the model is accurate in the abstract, but where it disagrees with the humans. Where the model flags players the team ignored and where it ignores players the team chased - those two lists are the conversation that determines whether the model gets adopted or quietly shelved.
Then what
After shadow mode, go to a limited live slice - one team, one segment - before full rollout. The staged path from shadow to slice to full is slower on paper and much faster in practice, because a model that loses the team's trust in week one takes months to recover.
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