Batch predictions look reasonable.
Real-time predictions don’t.
Same model, same features—supposedly. Yet results differ?
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Batch predictions look reasonable.
Real-time predictions don’t.
Same model, same features—supposedly. Yet results differ?
Differences usually stem from data freshness or preprocessing timing.
Batch jobs often use historical snapshots, while online systems use near-real-time data. Feature values may differ subtly but significantly.
Ensure both paths use the same feature definitions and time alignment rules.
The takeaway is that consistency requires shared assumptions across modes.