My speech-to-text model produces accurate transcripts when tested in a quiet office.However, when I try to use it in public places, accuracy drops sharply.Background noise causes words to be skipped or misheard.The model feels fragile outside controlled ...
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Some requests arrive with incomplete data.The model still returns predictions.But quality is unpredictable.I need a safer approach?
Once a customer agrees verbally, teams often assume the deal is complete.In reality, pricing approval, confirmation, and payment are distinct business events.Merging them can create confusion across finance and sales teams.
I upgraded to a GPU with much more VRAM.I increased the batch size to use the available memory.Now the training is noticeably slower per epoch.There are no errors, but performance feels worse than before.
