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.
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Batch predictions look reasonable.Real-time predictions don’t.Same model, same features—supposedly. Yet results differ?
The model still runs without errors.Performance seems “okay.”But I suspect it’s getting stale.There’s no obvious trigger.
I trained a CNN to classify multiple object categories from images.The training completes without errors and the accuracy looks decent.But when I run inference, every image gets the same label.Even very different images are predicted as the ...
The system performs well in offline tests.Under real user traffic, errors appear.Latency increases and predictions degrade.The same model is running.
I am training a convolutional neural network on a custom image dataset using PyTorch.For the first few batches the loss looks normal, but suddenly it becomes NaN and never recovers.There are no crashes or stack traces, only the ...
We fix the reported findings, but similar issues keep returning.Each test feels like starting over again.I’m trying to understand why progress feels stalled.
I’ve already removed SOQL queries and DML operations from loops in my Apex code. Despite that, I’m still hitting limits in real production scenarios with large datasets. The logic itself isn’t very complex, but the limits seem easy to hit. ...
Everyone is alerted quickly, but actual remediation takes longer than expected.Decisions feel slower and coordination breaks down under pressure.I want to understand what usually causes this and how teams improve response speed.
Kubernets pod restart