Tests pass, but production fails. I want to understand why
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A PyTorch inference script produces different outputs on every run.The model weights are loaded from the same file and the input tensor never changes.This only happens after moving from training to deployment.There are no errors or warnings.
Same logic exists in multiple places. I want to understand why.
Many teams still rely on spreadsheets or disconnected tools to track prospects and deals. This often leads to missed follow-ups, unclear ownership, and poor visibility into the pipeline.CRM users frequently hear about the Sales Module but don’t fully grasp ...
Every retraining run produces different artifacts.Code changes, data changes, and hyperparameters change too.Tracking what’s deployed is becoming confusing. Rollbacks are risky?
