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Quantitative Finance · Glosarium

Apa itu Shadow deployment, champion–challenger?

Dikenal juga sebagai: shadow deployment · champion--challenger

Definition 27.5 Machine Learning for Markets · Bab 27 — Monitoring and Retraining

In a shadow deployment a new model receives the production inputs and records its predictions, but its decisions are not executed; its outcomes are computed as if they had been. Champion–challenger is the discipline of keeping the production model (the champion) in place until a challenger, run beside it on the same data, has shown with a test chosen in advance that it is better.

Always-valid p-values of the challenger’s advantage over the champion, day by day in shadow after the reversal, five of the 20 runs; the test gives no verdict for ten days; dashed: 0.05, where the challenger is promoted. Data: ml_monitor.shadow_run.
Figure 27.2. Always-valid p-values of the challenger’s advantage over the champion, day by day in shadow after the reversal, five of the 20 runs; the test gives no verdict for ten days; dashed: 0.05, where the challenger is promoted. Data: ml_monitor.shadow_run.
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