Models are trained successfully.Deployment feels rushed.Problems surface late.The team loses momentum.
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Different teams trained models independently.Each performs well in certain cases.Now deployment is messy.Choosing one feels arbitrary.
My model works well during training and validation.But inference results differ even with similar inputs.There’s no obvious bug in the code.It feels like something subtle is off.
Demo orgs usually assume perfect data, linear flows, and cooperative users.Production orgs rarely behave this way once real pressure, volume, and edge cases appear.Many Salesforce issues surface only after go-live, not during demos.
Some formula fields calculate correctly for most records but return unexpected values for others. The formula itself hasn’t changed. The affected records don’t show obvious differences. I’m trying to understand what causes this inconsistency.
